diff --git a/config/env.js b/config/env.js
new file mode 100644
index 0000000..ca3edde
--- /dev/null
+++ b/config/env.js
@@ -0,0 +1,104 @@
+'use strict'
+
+const fs = require('fs')
+const path = require('path')
+const paths = require('./paths')
+
+// Make sure that including paths.js after env.js will read .env variables.
+delete require.cache[require.resolve('./paths')]
+
+const NODE_ENV = process.env.NODE_ENV
+if (!NODE_ENV) {
+  throw new Error(
+    'The NODE_ENV environment variable is required but was not specified.'
+  )
+}
+
+// https://github.com/bkeepers/dotenv#what-other-env-files-can-i-use
+const dotenvFiles = [
+  `${paths.dotenv}.${NODE_ENV}.local`,
+  // Don't include `.env.local` for `test` environment
+  // since normally you expect tests to produce the same
+  // results for everyone
+  NODE_ENV !== 'test' && `${paths.dotenv}.local`,
+  `${paths.dotenv}.${NODE_ENV}`,
+  paths.dotenv
+].filter(Boolean)
+
+// Load environment variables from .env* files. Suppress warnings using silent
+// if this file is missing. dotenv will never modify any environment variables
+// that have already been set.  Variable expansion is supported in .env files.
+// https://github.com/motdotla/dotenv
+// https://github.com/motdotla/dotenv-expand
+dotenvFiles.forEach(dotenvFile => {
+  if (fs.existsSync(dotenvFile)) {
+    require('dotenv-expand')(
+      require('dotenv').config({
+        path: dotenvFile
+      })
+    )
+  }
+})
+
+// We support resolving modules according to `NODE_PATH`.
+// This lets you use absolute paths in imports inside large monorepos:
+// https://github.com/facebook/create-react-app/issues/253.
+// It works similar to `NODE_PATH` in Node itself:
+// https://nodejs.org/api/modules.html#modules_loading_from_the_global_folders
+// Note that unlike in Node, only *relative* paths from `NODE_PATH` are honored.
+// Otherwise, we risk importing Node.js core modules into an app instead of webpack shims.
+// https://github.com/facebook/create-react-app/issues/1023#issuecomment-265344421
+// We also resolve them to make sure all tools using them work consistently.
+const appDirectory = fs.realpathSync(process.cwd())
+process.env.NODE_PATH = (process.env.NODE_PATH || '')
+  .split(path.delimiter)
+  .filter(folder => folder && !path.isAbsolute(folder))
+  .map(folder => path.resolve(appDirectory, folder))
+  .join(path.delimiter)
+
+// Grab NODE_ENV and REACT_APP_* environment variables and prepare them to be
+// injected into the application via DefinePlugin in webpack configuration.
+const REACT_APP = /^REACT_APP_/i
+
+function getClientEnvironment (publicUrl) {
+  const raw = Object.keys(process.env)
+    .filter(key => REACT_APP.test(key))
+    .reduce(
+      (env, key) => {
+        env[key] = process.env[key]
+        return env
+      },
+      {
+        // Useful for determining whether we’re running in production mode.
+        // Most importantly, it switches React into the correct mode.
+        NODE_ENV: process.env.NODE_ENV || 'development',
+        // Useful for resolving the correct path to static assets in `public`.
+        // For example, <img src={process.env.PUBLIC_URL + '/img/logo.png'} />.
+        // This should only be used as an escape hatch. Normally you would put
+        // images into the `src` and `import` them in code to get their paths.
+        PUBLIC_URL: publicUrl,
+        // We support configuring the sockjs pathname during development.
+        // These settings let a developer run multiple simultaneous projects.
+        // They are used as the connection `hostname`, `pathname` and `port`
+        // in webpackHotDevClient. They are used as the `sockHost`, `sockPath`
+        // and `sockPort` options in webpack-dev-server.
+        WDS_SOCKET_HOST: process.env.WDS_SOCKET_HOST,
+        WDS_SOCKET_PATH: process.env.WDS_SOCKET_PATH,
+        WDS_SOCKET_PORT: process.env.WDS_SOCKET_PORT,
+        // Whether or not react-refresh is enabled.
+        // It is defined here so it is available in the webpackHotDevClient.
+        FAST_REFRESH: process.env.FAST_REFRESH !== 'false'
+      }
+    )
+  // Stringify all values so we can feed into webpack DefinePlugin
+  const stringified = {
+    'process.env': Object.keys(raw).reduce((env, key) => {
+      env[key] = JSON.stringify(raw[key])
+      return env
+    }, {})
+  }
+
+  return { raw, stringified }
+}
+
+module.exports = getClientEnvironment
diff --git a/config/getHttpsConfig.js b/config/getHttpsConfig.js
new file mode 100644
index 0000000..5cdf120
--- /dev/null
+++ b/config/getHttpsConfig.js
@@ -0,0 +1,66 @@
+'use strict'
+
+const fs = require('fs')
+const path = require('path')
+const crypto = require('crypto')
+const chalk = require('react-dev-utils/chalk')
+const paths = require('./paths')
+
+// Ensure the certificate and key provided are valid and if not
+// throw an easy to debug error
+function validateKeyAndCerts ({ cert, key, keyFile, crtFile }) {
+  let encrypted
+  try {
+    // publicEncrypt will throw an error with an invalid cert
+    encrypted = crypto.publicEncrypt(cert, Buffer.from('test'))
+  } catch (err) {
+    throw new Error(
+      `The certificate "${chalk.yellow(crtFile)}" is invalid.\n${err.message}`
+    )
+  }
+
+  try {
+    // privateDecrypt will throw an error with an invalid key
+    crypto.privateDecrypt(key, encrypted)
+  } catch (err) {
+    throw new Error(
+      `The certificate key "${chalk.yellow(keyFile)}" is invalid.\n${
+        err.message
+      }`
+    )
+  }
+}
+
+// Read file and throw an error if it doesn't exist
+function readEnvFile (file, type) {
+  if (!fs.existsSync(file)) {
+    throw new Error(
+      `You specified ${chalk.cyan(
+        type
+      )} in your env, but the file "${chalk.yellow(file)}" can't be found.`
+    )
+  }
+  return fs.readFileSync(file)
+}
+
+// Get the https config
+// Return cert files if provided in env, otherwise just true or false
+function getHttpsConfig () {
+  const { SSL_CRT_FILE, SSL_KEY_FILE, HTTPS } = process.env
+  const isHttps = HTTPS === 'true'
+
+  if (isHttps && SSL_CRT_FILE && SSL_KEY_FILE) {
+    const crtFile = path.resolve(paths.appPath, SSL_CRT_FILE)
+    const keyFile = path.resolve(paths.appPath, SSL_KEY_FILE)
+    const config = {
+      cert: readEnvFile(crtFile, 'SSL_CRT_FILE'),
+      key: readEnvFile(keyFile, 'SSL_KEY_FILE')
+    }
+
+    validateKeyAndCerts({ ...config, keyFile, crtFile })
+    return config
+  }
+  return isHttps
+}
+
+module.exports = getHttpsConfig
diff --git a/config/jest/babelTransform.js b/config/jest/babelTransform.js
new file mode 100644
index 0000000..cd53a05
--- /dev/null
+++ b/config/jest/babelTransform.js
@@ -0,0 +1,29 @@
+'use strict'
+
+const babelJest = require('babel-jest').default
+
+const hasJsxRuntime = (() => {
+  if (process.env.DISABLE_NEW_JSX_TRANSFORM === 'true') {
+    return false
+  }
+
+  try {
+    require.resolve('react/jsx-runtime')
+    return true
+  } catch (e) {
+    return false
+  }
+})()
+
+module.exports = babelJest.createTransformer({
+  presets: [
+    [
+      require.resolve('babel-preset-react-app'),
+      {
+        runtime: hasJsxRuntime ? 'automatic' : 'classic'
+      }
+    ]
+  ],
+  babelrc: false,
+  configFile: false
+})
diff --git a/config/jest/cssTransform.js b/config/jest/cssTransform.js
new file mode 100644
index 0000000..10a0a4c
--- /dev/null
+++ b/config/jest/cssTransform.js
@@ -0,0 +1,14 @@
+'use strict'
+
+// This is a custom Jest transformer turning style imports into empty objects.
+// http://facebook.github.io/jest/docs/en/webpack.html
+
+module.exports = {
+  process () {
+    return 'module.exports = {};'
+  },
+  getCacheKey () {
+    // The output is always the same.
+    return 'cssTransform'
+  }
+}
diff --git a/config/jest/fileTransform.js b/config/jest/fileTransform.js
new file mode 100644
index 0000000..e1b1325
--- /dev/null
+++ b/config/jest/fileTransform.js
@@ -0,0 +1,40 @@
+'use strict'
+
+const path = require('path')
+const camelcase = require('camelcase')
+
+// This is a custom Jest transformer turning file imports into filenames.
+// http://facebook.github.io/jest/docs/en/webpack.html
+
+module.exports = {
+  process (src, filename) {
+    const assetFilename = JSON.stringify(path.basename(filename))
+
+    if (filename.match(/\.svg$/)) {
+      // Based on how SVGR generates a component name:
+      // https://github.com/smooth-code/svgr/blob/01b194cf967347d43d4cbe6b434404731b87cf27/packages/core/src/state.js#L6
+      const pascalCaseFilename = camelcase(path.parse(filename).name, {
+        pascalCase: true
+      })
+      const componentName = `Svg${pascalCaseFilename}`
+      return `const React = require('react');
+      module.exports = {
+        __esModule: true,
+        default: ${assetFilename},
+        ReactComponent: React.forwardRef(function ${componentName}(props, ref) {
+          return {
+            $$typeof: Symbol.for('react.element'),
+            type: 'svg',
+            ref: ref,
+            key: null,
+            props: Object.assign({}, props, {
+              children: ${assetFilename}
+            })
+          };
+        }),
+      };`
+    }
+
+    return `module.exports = ${assetFilename};`
+  }
+}
diff --git a/config/modules.js b/config/modules.js
new file mode 100644
index 0000000..fafd367
--- /dev/null
+++ b/config/modules.js
@@ -0,0 +1,134 @@
+'use strict'
+
+const fs = require('fs')
+const path = require('path')
+const paths = require('./paths')
+const chalk = require('react-dev-utils/chalk')
+const resolve = require('resolve')
+
+/**
+ * Get additional module paths based on the baseUrl of a compilerOptions object.
+ *
+ * @param {Object} options
+ */
+function getAdditionalModulePaths (options = {}) {
+  const baseUrl = options.baseUrl
+
+  if (!baseUrl) {
+    return ''
+  }
+
+  const baseUrlResolved = path.resolve(paths.appPath, baseUrl)
+
+  // We don't need to do anything if `baseUrl` is set to `node_modules`. This is
+  // the default behavior.
+  if (path.relative(paths.appNodeModules, baseUrlResolved) === '') {
+    return null
+  }
+
+  // Allow the user set the `baseUrl` to `appSrc`.
+  if (path.relative(paths.appSrc, baseUrlResolved) === '') {
+    return [paths.appSrc]
+  }
+
+  // If the path is equal to the root directory we ignore it here.
+  // We don't want to allow importing from the root directly as source files are
+  // not transpiled outside of `src`. We do allow importing them with the
+  // absolute path (e.g. `src/Components/Button.js`) but we set that up with
+  // an alias.
+  if (path.relative(paths.appPath, baseUrlResolved) === '') {
+    return null
+  }
+
+  // Otherwise, throw an error.
+  throw new Error(
+    chalk.red.bold(
+      "Your project's `baseUrl` can only be set to `src` or `node_modules`." +
+        ' Create React App does not support other values at this time.'
+    )
+  )
+}
+
+/**
+ * Get webpack aliases based on the baseUrl of a compilerOptions object.
+ *
+ * @param {*} options
+ */
+function getWebpackAliases (options = {}) {
+  const baseUrl = options.baseUrl
+
+  if (!baseUrl) {
+    return {}
+  }
+
+  const baseUrlResolved = path.resolve(paths.appPath, baseUrl)
+
+  if (path.relative(paths.appPath, baseUrlResolved) === '') {
+    return {
+      src: paths.appSrc
+    }
+  }
+}
+
+/**
+ * Get jest aliases based on the baseUrl of a compilerOptions object.
+ *
+ * @param {*} options
+ */
+function getJestAliases (options = {}) {
+  const baseUrl = options.baseUrl
+
+  if (!baseUrl) {
+    return {}
+  }
+
+  const baseUrlResolved = path.resolve(paths.appPath, baseUrl)
+
+  if (path.relative(paths.appPath, baseUrlResolved) === '') {
+    return {
+      '^src/(.*)$': '<rootDir>/src/$1'
+    }
+  }
+}
+
+function getModules () {
+  // Check if TypeScript is setup
+  const hasTsConfig = fs.existsSync(paths.appTsConfig)
+  const hasJsConfig = fs.existsSync(paths.appJsConfig)
+
+  if (hasTsConfig && hasJsConfig) {
+    throw new Error(
+      'You have both a tsconfig.json and a jsconfig.json. If you are using TypeScript please remove your jsconfig.json file.'
+    )
+  }
+
+  let config
+
+  // If there's a tsconfig.json we assume it's a
+  // TypeScript project and set up the config
+  // based on tsconfig.json
+  if (hasTsConfig) {
+    const ts = require(resolve.sync('typescript', {
+      basedir: paths.appNodeModules
+    }))
+    config = ts.readConfigFile(paths.appTsConfig, ts.sys.readFile).config
+    // Otherwise we'll check if there is jsconfig.json
+    // for non TS projects.
+  } else if (hasJsConfig) {
+    config = require(paths.appJsConfig)
+  }
+
+  config = config || {}
+  const options = config.compilerOptions || {}
+
+  const additionalModulePaths = getAdditionalModulePaths(options)
+
+  return {
+    additionalModulePaths,
+    webpackAliases: getWebpackAliases(options),
+    jestAliases: getJestAliases(options),
+    hasTsConfig
+  }
+}
+
+module.exports = getModules()
diff --git a/config/paths.js b/config/paths.js
new file mode 100644
index 0000000..bcd6d06
--- /dev/null
+++ b/config/paths.js
@@ -0,0 +1,75 @@
+'use strict'
+
+const path = require('path')
+const fs = require('fs')
+const getPublicUrlOrPath = require('react-dev-utils/getPublicUrlOrPath')
+
+// Make sure any symlinks in the project folder are resolved:
+// https://github.com/facebook/create-react-app/issues/637
+const appDirectory = fs.realpathSync(process.cwd())
+const resolveApp = relativePath => path.resolve(appDirectory, relativePath)
+
+// We use `PUBLIC_URL` environment variable or "homepage" field to infer
+// "public path" at which the app is served.
+// webpack needs to know it to put the right <script> hrefs into HTML even in
+// single-page apps that may serve index.html for nested URLs like /todos/42.
+// We can't use a relative path in HTML because we don't want to load something
+// like /todos/42/static/js/bundle.7289d.js. We have to know the root.
+const publicUrlOrPath = getPublicUrlOrPath(
+  process.env.NODE_ENV === 'development',
+  require(resolveApp('package.json')).homepage,
+  process.env.PUBLIC_URL
+)
+
+const buildPath = process.env.BUILD_PATH || 'build'
+
+const moduleFileExtensions = [
+  'web.mjs',
+  'mjs',
+  'web.js',
+  'js',
+  'web.ts',
+  'ts',
+  'web.tsx',
+  'tsx',
+  'json',
+  'web.jsx',
+  'jsx'
+]
+
+// Resolve file paths in the same order as webpack
+const resolveModule = (resolveFn, filePath) => {
+  const extension = moduleFileExtensions.find(extension =>
+    fs.existsSync(resolveFn(`${filePath}.${extension}`))
+  )
+
+  if (extension) {
+    return resolveFn(`${filePath}.${extension}`)
+  }
+
+  return resolveFn(`${filePath}.js`)
+}
+
+// config after eject: we're in ./config/
+module.exports = {
+  dotenv: resolveApp('.env'),
+  appPath: resolveApp('.'),
+  appBuild: resolveApp(buildPath),
+  appPublic: resolveApp('public'),
+  appHtml: resolveApp('public/index.html'),
+  appIndexJs: resolveModule(resolveApp, 'src/index'),
+  appPackageJson: resolveApp('package.json'),
+  appSrc: resolveApp('src'),
+  appTsConfig: resolveApp('tsconfig.json'),
+  appJsConfig: resolveApp('jsconfig.json'),
+  yarnLockFile: resolveApp('yarn.lock'),
+  testsSetup: resolveModule(resolveApp, 'src/setupTests'),
+  proxySetup: resolveApp('src/setupProxy.js'),
+  appNodeModules: resolveApp('node_modules'),
+  appWebpackCache: resolveApp('node_modules/.cache'),
+  appTsBuildInfoFile: resolveApp('node_modules/.cache/tsconfig.tsbuildinfo'),
+  swSrc: resolveModule(resolveApp, 'src/service-worker'),
+  publicUrlOrPath
+}
+
+module.exports.moduleFileExtensions = moduleFileExtensions
diff --git a/config/webpack.config.js b/config/webpack.config.js
new file mode 100644
index 0000000..9c9c268
--- /dev/null
+++ b/config/webpack.config.js
@@ -0,0 +1,755 @@
+'use strict'
+
+const fs = require('fs')
+const path = require('path')
+const webpack = require('webpack')
+const resolve = require('resolve')
+const HtmlWebpackPlugin = require('html-webpack-plugin')
+const CaseSensitivePathsPlugin = require('case-sensitive-paths-webpack-plugin')
+const InlineChunkHtmlPlugin = require('react-dev-utils/InlineChunkHtmlPlugin')
+const TerserPlugin = require('terser-webpack-plugin')
+const MiniCssExtractPlugin = require('mini-css-extract-plugin')
+const CssMinimizerPlugin = require('css-minimizer-webpack-plugin')
+const { WebpackManifestPlugin } = require('webpack-manifest-plugin')
+const InterpolateHtmlPlugin = require('react-dev-utils/InterpolateHtmlPlugin')
+const WorkboxWebpackPlugin = require('workbox-webpack-plugin')
+const ModuleScopePlugin = require('react-dev-utils/ModuleScopePlugin')
+const getCSSModuleLocalIdent = require('react-dev-utils/getCSSModuleLocalIdent')
+const ESLintPlugin = require('eslint-webpack-plugin')
+const paths = require('./paths')
+const modules = require('./modules')
+const getClientEnvironment = require('./env')
+const ModuleNotFoundPlugin = require('react-dev-utils/ModuleNotFoundPlugin')
+const ForkTsCheckerWebpackPlugin =
+  process.env.TSC_COMPILE_ON_ERROR === 'true'
+    ? require('react-dev-utils/ForkTsCheckerWarningWebpackPlugin')
+    : require('react-dev-utils/ForkTsCheckerWebpackPlugin')
+const ReactRefreshWebpackPlugin = require('@pmmmwh/react-refresh-webpack-plugin')
+
+const createEnvironmentHash = require('./webpack/persistentCache/createEnvironmentHash')
+
+// Source maps are resource heavy and can cause out of memory issue for large source files.
+const shouldUseSourceMap = process.env.GENERATE_SOURCEMAP !== 'false'
+
+const reactRefreshRuntimeEntry = require.resolve('react-refresh/runtime')
+const reactRefreshWebpackPluginRuntimeEntry = require.resolve(
+  '@pmmmwh/react-refresh-webpack-plugin'
+)
+const babelRuntimeEntry = require.resolve('babel-preset-react-app')
+const babelRuntimeEntryHelpers = require.resolve(
+  '@babel/runtime/helpers/esm/assertThisInitialized',
+  { paths: [babelRuntimeEntry] }
+)
+const babelRuntimeRegenerator = require.resolve('@babel/runtime/regenerator', {
+  paths: [babelRuntimeEntry]
+})
+
+// Some apps do not need the benefits of saving a web request, so not inlining the chunk
+// makes for a smoother build process.
+const shouldInlineRuntimeChunk = process.env.INLINE_RUNTIME_CHUNK !== 'false'
+
+const emitErrorsAsWarnings = process.env.ESLINT_NO_DEV_ERRORS === 'true'
+const disableESLintPlugin = process.env.DISABLE_ESLINT_PLUGIN === 'true'
+
+const imageInlineSizeLimit = parseInt(
+  process.env.IMAGE_INLINE_SIZE_LIMIT || '10000'
+)
+
+// Check if TypeScript is setup
+const useTypeScript = fs.existsSync(paths.appTsConfig)
+
+// Check if Tailwind config exists
+const useTailwind = fs.existsSync(
+  path.join(paths.appPath, 'tailwind.config.js')
+)
+
+// Get the path to the uncompiled service worker (if it exists).
+const swSrc = paths.swSrc
+
+// style files regexes
+const cssRegex = /\.css$/
+const cssModuleRegex = /\.module\.css$/
+const sassRegex = /\.(scss|sass)$/
+const sassModuleRegex = /\.module\.(scss|sass)$/
+
+const hasJsxRuntime = (() => {
+  if (process.env.DISABLE_NEW_JSX_TRANSFORM === 'true') {
+    return false
+  }
+
+  try {
+    require.resolve('react/jsx-runtime')
+    return true
+  } catch (e) {
+    return false
+  }
+})()
+
+// This is the production and development configuration.
+// It is focused on developer experience, fast rebuilds, and a minimal bundle.
+module.exports = function (webpackEnv) {
+  const isEnvDevelopment = webpackEnv === 'development'
+  const isEnvProduction = webpackEnv === 'production'
+
+  // Variable used for enabling profiling in Production
+  // passed into alias object. Uses a flag if passed into the build command
+  const isEnvProductionProfile =
+    isEnvProduction && process.argv.includes('--profile')
+
+  // We will provide `paths.publicUrlOrPath` to our app
+  // as %PUBLIC_URL% in `index.html` and `process.env.PUBLIC_URL` in JavaScript.
+  // Omit trailing slash as %PUBLIC_URL%/xyz looks better than %PUBLIC_URL%xyz.
+  // Get environment variables to inject into our app.
+  const env = getClientEnvironment(paths.publicUrlOrPath.slice(0, -1))
+
+  const shouldUseReactRefresh = env.raw.FAST_REFRESH
+
+  // common function to get style loaders
+  const getStyleLoaders = (cssOptions, preProcessor) => {
+    const loaders = [
+      isEnvDevelopment && require.resolve('style-loader'),
+      isEnvProduction && {
+        loader: MiniCssExtractPlugin.loader,
+        // css is located in `static/css`, use '../../' to locate index.html folder
+        // in production `paths.publicUrlOrPath` can be a relative path
+        options: paths.publicUrlOrPath.startsWith('.')
+          ? { publicPath: '../../' }
+          : {}
+      },
+      {
+        loader: require.resolve('css-loader'),
+        options: cssOptions
+      },
+      {
+        // Options for PostCSS as we reference these options twice
+        // Adds vendor prefixing based on your specified browser support in
+        // package.json
+        loader: require.resolve('postcss-loader'),
+        options: {
+          postcssOptions: {
+            // Necessary for external CSS imports to work
+            // https://github.com/facebook/create-react-app/issues/2677
+            ident: 'postcss',
+            config: false,
+            plugins: !useTailwind
+              ? [
+                  'postcss-flexbugs-fixes',
+                  [
+                    'postcss-preset-env',
+                    {
+                      autoprefixer: {
+                        flexbox: 'no-2009'
+                      },
+                      stage: 3
+                    }
+                  ],
+                  // Adds PostCSS Normalize as the reset css with default options,
+                  // so that it honors browserslist config in package.json
+                  // which in turn let's users customize the target behavior as per their needs.
+                  'postcss-normalize'
+                ]
+              : [
+                  'tailwindcss',
+                  'postcss-flexbugs-fixes',
+                  [
+                    'postcss-preset-env',
+                    {
+                      autoprefixer: {
+                        flexbox: 'no-2009'
+                      },
+                      stage: 3
+                    }
+                  ]
+                ]
+          },
+          sourceMap: isEnvProduction ? shouldUseSourceMap : isEnvDevelopment
+        }
+      }
+    ].filter(Boolean)
+    if (preProcessor) {
+      loaders.push(
+        {
+          loader: require.resolve('resolve-url-loader'),
+          options: {
+            sourceMap: isEnvProduction ? shouldUseSourceMap : isEnvDevelopment,
+            root: paths.appSrc
+          }
+        },
+        {
+          loader: require.resolve(preProcessor),
+          options: {
+            sourceMap: true
+          }
+        }
+      )
+    }
+    return loaders
+  }
+
+  return {
+    target: ['browserslist'],
+    // Webpack noise constrained to errors and warnings
+    stats: 'errors-warnings',
+    mode: isEnvProduction ? 'production' : isEnvDevelopment && 'development',
+    // Stop compilation early in production
+    bail: isEnvProduction,
+    devtool: isEnvProduction
+      ? shouldUseSourceMap
+        ? 'source-map'
+        : false
+      : isEnvDevelopment && 'cheap-module-source-map',
+    // These are the "entry points" to our application.
+    // This means they will be the "root" imports that are included in JS bundle.
+    entry: paths.appIndexJs,
+    output: {
+      // The build folder.
+      path: paths.appBuild,
+      // Add /* filename */ comments to generated require()s in the output.
+      pathinfo: isEnvDevelopment,
+      // There will be one main bundle, and one file per asynchronous chunk.
+      // In development, it does not produce real files.
+      filename: isEnvProduction
+        ? 'static/js/[name].[contenthash:8].js'
+        : isEnvDevelopment && 'static/js/bundle.js',
+      // There are also additional JS chunk files if you use code splitting.
+      chunkFilename: isEnvProduction
+        ? 'static/js/[name].[contenthash:8].chunk.js'
+        : isEnvDevelopment && 'static/js/[name].chunk.js',
+      assetModuleFilename: 'static/media/[name].[hash][ext]',
+      // webpack uses `publicPath` to determine where the app is being served from.
+      // It requires a trailing slash, or the file assets will get an incorrect path.
+      // We inferred the "public path" (such as / or /my-project) from homepage.
+      publicPath: paths.publicUrlOrPath,
+      // Point sourcemap entries to original disk location (format as URL on Windows)
+      devtoolModuleFilenameTemplate: isEnvProduction
+        ? info =>
+          path
+            .relative(paths.appSrc, info.absoluteResourcePath)
+            .replace(/\\/g, '/')
+        : isEnvDevelopment &&
+          (info => path.resolve(info.absoluteResourcePath).replace(/\\/g, '/'))
+    },
+    cache: {
+      type: 'filesystem',
+      version: createEnvironmentHash(env.raw),
+      cacheDirectory: paths.appWebpackCache,
+      store: 'pack',
+      buildDependencies: {
+        defaultWebpack: ['webpack/lib/'],
+        config: [__filename],
+        tsconfig: [paths.appTsConfig, paths.appJsConfig].filter(f =>
+          fs.existsSync(f)
+        )
+      }
+    },
+    infrastructureLogging: {
+      level: 'none'
+    },
+    optimization: {
+      minimize: isEnvProduction,
+      minimizer: [
+        // This is only used in production mode
+        new TerserPlugin({
+          terserOptions: {
+            parse: {
+              // We want terser to parse ecma 8 code. However, we don't want it
+              // to apply any minification steps that turns valid ecma 5 code
+              // into invalid ecma 5 code. This is why the 'compress' and 'output'
+              // sections only apply transformations that are ecma 5 safe
+              // https://github.com/facebook/create-react-app/pull/4234
+              ecma: 8
+            },
+            compress: {
+              ecma: 5,
+              warnings: false,
+              // Disabled because of an issue with Uglify breaking seemingly valid code:
+              // https://github.com/facebook/create-react-app/issues/2376
+              // Pending further investigation:
+              // https://github.com/mishoo/UglifyJS2/issues/2011
+              comparisons: false,
+              // Disabled because of an issue with Terser breaking valid code:
+              // https://github.com/facebook/create-react-app/issues/5250
+              // Pending further investigation:
+              // https://github.com/terser-js/terser/issues/120
+              inline: 2
+            },
+            mangle: {
+              safari10: true
+            },
+            // Added for profiling in devtools
+            keep_classnames: isEnvProductionProfile,
+            keep_fnames: isEnvProductionProfile,
+            output: {
+              ecma: 5,
+              comments: false,
+              // Turned on because emoji and regex is not minified properly using default
+              // https://github.com/facebook/create-react-app/issues/2488
+              ascii_only: true
+            }
+          }
+        }),
+        // This is only used in production mode
+        new CssMinimizerPlugin()
+      ]
+    },
+    resolve: {
+      // This allows you to set a fallback for where webpack should look for modules.
+      // We placed these paths second because we want `node_modules` to "win"
+      // if there are any conflicts. This matches Node resolution mechanism.
+      // https://github.com/facebook/create-react-app/issues/253
+      modules: ['node_modules', paths.appNodeModules].concat(
+        modules.additionalModulePaths || []
+      ),
+      // These are the reasonable defaults supported by the Node ecosystem.
+      // We also include JSX as a common component filename extension to support
+      // some tools, although we do not recommend using it, see:
+      // https://github.com/facebook/create-react-app/issues/290
+      // `web` extension prefixes have been added for better support
+      // for React Native Web.
+      extensions: paths.moduleFileExtensions
+        .map(ext => `.${ext}`)
+        .filter(ext => useTypeScript || !ext.includes('ts')),
+      alias: {
+        // Support React Native Web
+        // https://www.smashingmagazine.com/2016/08/a-glimpse-into-the-future-with-react-native-for-web/
+        'react-native': 'react-native-web',
+        // Allows for better profiling with ReactDevTools
+        ...(isEnvProductionProfile && {
+          'react-dom$': 'react-dom/profiling',
+          'scheduler/tracing': 'scheduler/tracing-profiling'
+        }),
+        ...(modules.webpackAliases || {})
+      },
+      plugins: [
+        // Prevents users from importing files from outside of src/ (or node_modules/).
+        // This often causes confusion because we only process files within src/ with babel.
+        // To fix this, we prevent you from importing files out of src/ -- if you'd like to,
+        // please link the files into your node_modules/ and let module-resolution kick in.
+        // Make sure your source files are compiled, as they will not be processed in any way.
+        new ModuleScopePlugin(paths.appSrc, [
+          paths.appPackageJson,
+          reactRefreshRuntimeEntry,
+          reactRefreshWebpackPluginRuntimeEntry,
+          babelRuntimeEntry,
+          babelRuntimeEntryHelpers,
+          babelRuntimeRegenerator
+        ])
+      ]
+    },
+    module: {
+      strictExportPresence: true,
+      rules: [
+        // Handle node_modules packages that contain sourcemaps
+        shouldUseSourceMap && {
+          enforce: 'pre',
+          exclude: /@babel(?:\/|\\{1,2})runtime/,
+          test: /\.(js|mjs|jsx|ts|tsx|css)$/,
+          loader: require.resolve('source-map-loader')
+        },
+        {
+          // "oneOf" will traverse all following loaders until one will
+          // match the requirements. When no loader matches it will fall
+          // back to the "file" loader at the end of the loader list.
+          oneOf: [
+            // TODO: Merge this config once `image/avif` is in the mime-db
+            // https://github.com/jshttp/mime-db
+            {
+              test: [/\.avif$/],
+              type: 'asset',
+              mimetype: 'image/avif',
+              parser: {
+                dataUrlCondition: {
+                  maxSize: imageInlineSizeLimit
+                }
+              }
+            },
+            // "url" loader works like "file" loader except that it embeds assets
+            // smaller than specified limit in bytes as data URLs to avoid requests.
+            // A missing `test` is equivalent to a match.
+            {
+              test: [/\.bmp$/, /\.gif$/, /\.jpe?g$/, /\.png$/],
+              type: 'asset',
+              parser: {
+                dataUrlCondition: {
+                  maxSize: imageInlineSizeLimit
+                }
+              }
+            },
+            {
+              test: /\.svg$/,
+              use: [
+                {
+                  loader: require.resolve('@svgr/webpack'),
+                  options: {
+                    prettier: false,
+                    svgo: false,
+                    svgoConfig: {
+                      plugins: [{ removeViewBox: false }]
+                    },
+                    titleProp: true,
+                    ref: true
+                  }
+                },
+                {
+                  loader: require.resolve('file-loader'),
+                  options: {
+                    name: 'static/media/[name].[hash].[ext]'
+                  }
+                }
+              ],
+              issuer: {
+                and: [/\.(ts|tsx|js|jsx|md|mdx)$/]
+              }
+            },
+            // Process application JS with Babel.
+            // The preset includes JSX, Flow, TypeScript, and some ESnext features.
+            {
+              test: /\.(js|mjs|jsx|ts|tsx)$/,
+              include: paths.appSrc,
+              loader: require.resolve('babel-loader'),
+              options: {
+                customize: require.resolve(
+                  'babel-preset-react-app/webpack-overrides'
+                ),
+                presets: [
+                  [
+                    require.resolve('babel-preset-react-app'),
+                    {
+                      runtime: hasJsxRuntime ? 'automatic' : 'classic'
+                    }
+                  ]
+                ],
+
+                plugins: [
+                  isEnvDevelopment &&
+                    shouldUseReactRefresh &&
+                    require.resolve('react-refresh/babel')
+                ].filter(Boolean),
+                // This is a feature of `babel-loader` for webpack (not Babel itself).
+                // It enables caching results in ./node_modules/.cache/babel-loader/
+                // directory for faster rebuilds.
+                cacheDirectory: true,
+                // See #6846 for context on why cacheCompression is disabled
+                cacheCompression: false,
+                compact: isEnvProduction
+              }
+            },
+            // Process any JS outside of the app with Babel.
+            // Unlike the application JS, we only compile the standard ES features.
+            {
+              test: /\.(js|mjs)$/,
+              exclude: /@babel(?:\/|\\{1,2})runtime/,
+              loader: require.resolve('babel-loader'),
+              options: {
+                babelrc: false,
+                configFile: false,
+                compact: false,
+                presets: [
+                  [
+                    require.resolve('babel-preset-react-app/dependencies'),
+                    { helpers: true }
+                  ]
+                ],
+                cacheDirectory: true,
+                // See #6846 for context on why cacheCompression is disabled
+                cacheCompression: false,
+
+                // Babel sourcemaps are needed for debugging into node_modules
+                // code.  Without the options below, debuggers like VSCode
+                // show incorrect code and set breakpoints on the wrong lines.
+                sourceMaps: shouldUseSourceMap,
+                inputSourceMap: shouldUseSourceMap
+              }
+            },
+            // "postcss" loader applies autoprefixer to our CSS.
+            // "css" loader resolves paths in CSS and adds assets as dependencies.
+            // "style" loader turns CSS into JS modules that inject <style> tags.
+            // In production, we use MiniCSSExtractPlugin to extract that CSS
+            // to a file, but in development "style" loader enables hot editing
+            // of CSS.
+            // By default we support CSS Modules with the extension .module.css
+            {
+              test: cssRegex,
+              exclude: cssModuleRegex,
+              use: getStyleLoaders({
+                importLoaders: 1,
+                sourceMap: isEnvProduction
+                  ? shouldUseSourceMap
+                  : isEnvDevelopment,
+                modules: {
+                  mode: 'icss'
+                }
+              }),
+              // Don't consider CSS imports dead code even if the
+              // containing package claims to have no side effects.
+              // Remove this when webpack adds a warning or an error for this.
+              // See https://github.com/webpack/webpack/issues/6571
+              sideEffects: true
+            },
+            // Adds support for CSS Modules (https://github.com/css-modules/css-modules)
+            // using the extension .module.css
+            {
+              test: cssModuleRegex,
+              use: getStyleLoaders({
+                importLoaders: 1,
+                sourceMap: isEnvProduction
+                  ? shouldUseSourceMap
+                  : isEnvDevelopment,
+                modules: {
+                  mode: 'local',
+                  getLocalIdent: getCSSModuleLocalIdent
+                }
+              })
+            },
+            // Opt-in support for SASS (using .scss or .sass extensions).
+            // By default we support SASS Modules with the
+            // extensions .module.scss or .module.sass
+            {
+              test: sassRegex,
+              exclude: sassModuleRegex,
+              use: getStyleLoaders(
+                {
+                  importLoaders: 3,
+                  sourceMap: isEnvProduction
+                    ? shouldUseSourceMap
+                    : isEnvDevelopment,
+                  modules: {
+                    mode: 'icss'
+                  }
+                },
+                'sass-loader'
+              ),
+              // Don't consider CSS imports dead code even if the
+              // containing package claims to have no side effects.
+              // Remove this when webpack adds a warning or an error for this.
+              // See https://github.com/webpack/webpack/issues/6571
+              sideEffects: true
+            },
+            // Adds support for CSS Modules, but using SASS
+            // using the extension .module.scss or .module.sass
+            {
+              test: sassModuleRegex,
+              use: getStyleLoaders(
+                {
+                  importLoaders: 3,
+                  sourceMap: isEnvProduction
+                    ? shouldUseSourceMap
+                    : isEnvDevelopment,
+                  modules: {
+                    mode: 'local',
+                    getLocalIdent: getCSSModuleLocalIdent
+                  }
+                },
+                'sass-loader'
+              )
+            },
+            // "file" loader makes sure those assets get served by WebpackDevServer.
+            // When you `import` an asset, you get its (virtual) filename.
+            // In production, they would get copied to the `build` folder.
+            // This loader doesn't use a "test" so it will catch all modules
+            // that fall through the other loaders.
+            {
+              // Exclude `js` files to keep "css" loader working as it injects
+              // its runtime that would otherwise be processed through "file" loader.
+              // Also exclude `html` and `json` extensions so they get processed
+              // by webpacks internal loaders.
+              exclude: [/^$/, /\.(js|mjs|jsx|ts|tsx)$/, /\.html$/, /\.json$/],
+              type: 'asset/resource'
+            }
+            // ** STOP ** Are you adding a new loader?
+            // Make sure to add the new loader(s) before the "file" loader.
+          ]
+        }
+      ].filter(Boolean)
+    },
+    plugins: [
+      // Generates an `index.html` file with the <script> injected.
+      new HtmlWebpackPlugin(
+        Object.assign(
+          {},
+          {
+            inject: true,
+            template: paths.appHtml
+          },
+          isEnvProduction
+            ? {
+                minify: {
+                  removeComments: true,
+                  collapseWhitespace: true,
+                  removeRedundantAttributes: true,
+                  useShortDoctype: true,
+                  removeEmptyAttributes: true,
+                  removeStyleLinkTypeAttributes: true,
+                  keepClosingSlash: true,
+                  minifyJS: true,
+                  minifyCSS: true,
+                  minifyURLs: true
+                }
+              }
+            : undefined
+        )
+      ),
+      // Inlines the webpack runtime script. This script is too small to warrant
+      // a network request.
+      // https://github.com/facebook/create-react-app/issues/5358
+      isEnvProduction &&
+        shouldInlineRuntimeChunk &&
+        new InlineChunkHtmlPlugin(HtmlWebpackPlugin, [/runtime-.+[.]js/]),
+      // Makes some environment variables available in index.html.
+      // The public URL is available as %PUBLIC_URL% in index.html, e.g.:
+      // <link rel="icon" href="%PUBLIC_URL%/favicon.ico">
+      // It will be an empty string unless you specify "homepage"
+      // in `package.json`, in which case it will be the pathname of that URL.
+      new InterpolateHtmlPlugin(HtmlWebpackPlugin, env.raw),
+      // This gives some necessary context to module not found errors, such as
+      // the requesting resource.
+      new ModuleNotFoundPlugin(paths.appPath),
+      // Makes some environment variables available to the JS code, for example:
+      // if (process.env.NODE_ENV === 'production') { ... }. See `./env.js`.
+      // It is absolutely essential that NODE_ENV is set to production
+      // during a production build.
+      // Otherwise React will be compiled in the very slow development mode.
+      new webpack.DefinePlugin(env.stringified),
+      // Experimental hot reloading for React .
+      // https://github.com/facebook/react/tree/main/packages/react-refresh
+      isEnvDevelopment &&
+        shouldUseReactRefresh &&
+        new ReactRefreshWebpackPlugin({
+          overlay: false
+        }),
+      // Watcher doesn't work well if you mistype casing in a path so we use
+      // a plugin that prints an error when you attempt to do this.
+      // See https://github.com/facebook/create-react-app/issues/240
+      isEnvDevelopment && new CaseSensitivePathsPlugin(),
+      isEnvProduction &&
+        new MiniCssExtractPlugin({
+          // Options similar to the same options in webpackOptions.output
+          // both options are optional
+          filename: 'static/css/[name].[contenthash:8].css',
+          chunkFilename: 'static/css/[name].[contenthash:8].chunk.css'
+        }),
+      // Generate an asset manifest file with the following content:
+      // - "files" key: Mapping of all asset filenames to their corresponding
+      //   output file so that tools can pick it up without having to parse
+      //   `index.html`
+      // - "entrypoints" key: Array of files which are included in `index.html`,
+      //   can be used to reconstruct the HTML if necessary
+      new WebpackManifestPlugin({
+        fileName: 'asset-manifest.json',
+        publicPath: paths.publicUrlOrPath,
+        generate: (seed, files, entrypoints) => {
+          const manifestFiles = files.reduce((manifest, file) => {
+            manifest[file.name] = file.path
+            return manifest
+          }, seed)
+          const entrypointFiles = entrypoints.main.filter(
+            fileName => !fileName.endsWith('.map')
+          )
+
+          return {
+            files: manifestFiles,
+            entrypoints: entrypointFiles
+          }
+        }
+      }),
+      // Moment.js is an extremely popular library that bundles large locale files
+      // by default due to how webpack interprets its code. This is a practical
+      // solution that requires the user to opt into importing specific locales.
+      // https://github.com/jmblog/how-to-optimize-momentjs-with-webpack
+      // You can remove this if you don't use Moment.js:
+      new webpack.IgnorePlugin({
+        resourceRegExp: /^\.\/locale$/,
+        contextRegExp: /moment$/
+      }),
+      // Generate a service worker script that will precache, and keep up to date,
+      // the HTML & assets that are part of the webpack build.
+      isEnvProduction &&
+        fs.existsSync(swSrc) &&
+        new WorkboxWebpackPlugin.InjectManifest({
+          swSrc,
+          dontCacheBustURLsMatching: /\.[0-9a-f]{8}\./,
+          exclude: [/\.map$/, /asset-manifest\.json$/, /LICENSE/],
+          // Bump up the default maximum size (2mb) that's precached,
+          // to make lazy-loading failure scenarios less likely.
+          // See https://github.com/cra-template/pwa/issues/13#issuecomment-722667270
+          maximumFileSizeToCacheInBytes: 5 * 1024 * 1024
+        }),
+      // TypeScript type checking
+      useTypeScript &&
+        new ForkTsCheckerWebpackPlugin({
+          async: isEnvDevelopment,
+          typescript: {
+            typescriptPath: resolve.sync('typescript', {
+              basedir: paths.appNodeModules
+            }),
+            configOverwrite: {
+              compilerOptions: {
+                sourceMap: isEnvProduction
+                  ? shouldUseSourceMap
+                  : isEnvDevelopment,
+                skipLibCheck: true,
+                inlineSourceMap: false,
+                declarationMap: false,
+                noEmit: true,
+                incremental: true,
+                tsBuildInfoFile: paths.appTsBuildInfoFile
+              }
+            },
+            context: paths.appPath,
+            diagnosticOptions: {
+              syntactic: true
+            },
+            mode: 'write-references'
+            // profile: true,
+          },
+          issue: {
+            // This one is specifically to match during CI tests,
+            // as micromatch doesn't match
+            // '../cra-template-typescript/template/src/App.tsx'
+            // otherwise.
+            include: [
+              { file: '../**/src/**/*.{ts,tsx}' },
+              { file: '**/src/**/*.{ts,tsx}' }
+            ],
+            exclude: [
+              { file: '**/src/**/__tests__/**' },
+              { file: '**/src/**/?(*.){spec|test}.*' },
+              { file: '**/src/setupProxy.*' },
+              { file: '**/src/setupTests.*' }
+            ]
+          },
+          logger: {
+            infrastructure: 'silent'
+          }
+        }),
+      !disableESLintPlugin &&
+        new ESLintPlugin({
+          // Plugin options
+          extensions: ['js', 'mjs', 'jsx', 'ts', 'tsx'],
+          formatter: require.resolve('react-dev-utils/eslintFormatter'),
+          eslintPath: require.resolve('eslint'),
+          failOnError: !(isEnvDevelopment && emitErrorsAsWarnings),
+          context: paths.appSrc,
+          cache: true,
+          cacheLocation: path.resolve(
+            paths.appNodeModules,
+            '.cache/.eslintcache'
+          ),
+          // ESLint class options
+          cwd: paths.appPath,
+          resolvePluginsRelativeTo: __dirname,
+          baseConfig: {
+            extends: [require.resolve('eslint-config-react-app/base')],
+            rules: {
+              ...(!hasJsxRuntime && {
+                'react/react-in-jsx-scope': 'error'
+              })
+            }
+          }
+        })
+    ].filter(Boolean),
+    // Turn off performance processing because we utilize
+    // our own hints via the FileSizeReporter
+    performance: false
+  }
+}
diff --git a/config/webpack/persistentCache/createEnvironmentHash.js b/config/webpack/persistentCache/createEnvironmentHash.js
new file mode 100644
index 0000000..7073be1
--- /dev/null
+++ b/config/webpack/persistentCache/createEnvironmentHash.js
@@ -0,0 +1,9 @@
+'use strict'
+const { createHash } = require('crypto')
+
+module.exports = env => {
+  const hash = createHash('md5')
+  hash.update(JSON.stringify(env))
+
+  return hash.digest('hex')
+}
diff --git a/config/webpackDevServer.config.js b/config/webpackDevServer.config.js
new file mode 100644
index 0000000..689c502
--- /dev/null
+++ b/config/webpackDevServer.config.js
@@ -0,0 +1,127 @@
+'use strict'
+
+const fs = require('fs')
+const evalSourceMapMiddleware = require('react-dev-utils/evalSourceMapMiddleware')
+const noopServiceWorkerMiddleware = require('react-dev-utils/noopServiceWorkerMiddleware')
+const ignoredFiles = require('react-dev-utils/ignoredFiles')
+const redirectServedPath = require('react-dev-utils/redirectServedPathMiddleware')
+const paths = require('./paths')
+const getHttpsConfig = require('./getHttpsConfig')
+
+const host = process.env.HOST || '0.0.0.0'
+const sockHost = process.env.WDS_SOCKET_HOST
+const sockPath = process.env.WDS_SOCKET_PATH // default: '/ws'
+const sockPort = process.env.WDS_SOCKET_PORT
+
+module.exports = function (proxy, allowedHost) {
+  const disableFirewall =
+    !proxy || process.env.DANGEROUSLY_DISABLE_HOST_CHECK === 'true'
+  return {
+    // WebpackDevServer 2.4.3 introduced a security fix that prevents remote
+    // websites from potentially accessing local content through DNS rebinding:
+    // https://github.com/webpack/webpack-dev-server/issues/887
+    // https://medium.com/webpack/webpack-dev-server-middleware-security-issues-1489d950874a
+    // However, it made several existing use cases such as development in cloud
+    // environment or subdomains in development significantly more complicated:
+    // https://github.com/facebook/create-react-app/issues/2271
+    // https://github.com/facebook/create-react-app/issues/2233
+    // While we're investigating better solutions, for now we will take a
+    // compromise. Since our WDS configuration only serves files in the `public`
+    // folder we won't consider accessing them a vulnerability. However, if you
+    // use the `proxy` feature, it gets more dangerous because it can expose
+    // remote code execution vulnerabilities in backends like Django and Rails.
+    // So we will disable the host check normally, but enable it if you have
+    // specified the `proxy` setting. Finally, we let you override it if you
+    // really know what you're doing with a special environment variable.
+    // Note: ["localhost", ".localhost"] will support subdomains - but we might
+    // want to allow setting the allowedHosts manually for more complex setups
+    allowedHosts: disableFirewall ? 'all' : [allowedHost],
+    headers: {
+      'Access-Control-Allow-Origin': '*',
+      'Access-Control-Allow-Methods': '*',
+      'Access-Control-Allow-Headers': '*'
+    },
+    // Enable gzip compression of generated files.
+    compress: true,
+    static: {
+      // By default WebpackDevServer serves physical files from current directory
+      // in addition to all the virtual build products that it serves from memory.
+      // This is confusing because those files won’t automatically be available in
+      // production build folder unless we copy them. However, copying the whole
+      // project directory is dangerous because we may expose sensitive files.
+      // Instead, we establish a convention that only files in `public` directory
+      // get served. Our build script will copy `public` into the `build` folder.
+      // In `index.html`, you can get URL of `public` folder with %PUBLIC_URL%:
+      // <link rel="icon" href="%PUBLIC_URL%/favicon.ico">
+      // In JavaScript code, you can access it with `process.env.PUBLIC_URL`.
+      // Note that we only recommend to use `public` folder as an escape hatch
+      // for files like `favicon.ico`, `manifest.json`, and libraries that are
+      // for some reason broken when imported through webpack. If you just want to
+      // use an image, put it in `src` and `import` it from JavaScript instead.
+      directory: paths.appPublic,
+      publicPath: [paths.publicUrlOrPath],
+      // By default files from `contentBase` will not trigger a page reload.
+      watch: {
+        // Reportedly, this avoids CPU overload on some systems.
+        // https://github.com/facebook/create-react-app/issues/293
+        // src/node_modules is not ignored to support absolute imports
+        // https://github.com/facebook/create-react-app/issues/1065
+        ignored: ignoredFiles(paths.appSrc)
+      }
+    },
+    client: {
+      webSocketURL: {
+        // Enable custom sockjs pathname for websocket connection to hot reloading server.
+        // Enable custom sockjs hostname, pathname and port for websocket connection
+        // to hot reloading server.
+        hostname: sockHost,
+        pathname: sockPath,
+        port: sockPort
+      },
+      overlay: {
+        errors: true,
+        warnings: false
+      }
+    },
+    devMiddleware: {
+      // It is important to tell WebpackDevServer to use the same "publicPath" path as
+      // we specified in the webpack config. When homepage is '.', default to serving
+      // from the root.
+      // remove last slash so user can land on `/test` instead of `/test/`
+      publicPath: paths.publicUrlOrPath.slice(0, -1)
+    },
+
+    https: getHttpsConfig(),
+    host,
+    historyApiFallback: {
+      // Paths with dots should still use the history fallback.
+      // See https://github.com/facebook/create-react-app/issues/387.
+      disableDotRule: true,
+      index: paths.publicUrlOrPath
+    },
+    // `proxy` is run between `before` and `after` `webpack-dev-server` hooks
+    proxy,
+    onBeforeSetupMiddleware (devServer) {
+      // Keep `evalSourceMapMiddleware`
+      // middlewares before `redirectServedPath` otherwise will not have any effect
+      // This lets us fetch source contents from webpack for the error overlay
+      devServer.app.use(evalSourceMapMiddleware(devServer))
+
+      if (fs.existsSync(paths.proxySetup)) {
+        // This registers user provided middleware for proxy reasons
+        require(paths.proxySetup)(devServer.app)
+      }
+    },
+    onAfterSetupMiddleware (devServer) {
+      // Redirect to `PUBLIC_URL` or `homepage` from `package.json` if url not match
+      devServer.app.use(redirectServedPath(paths.publicUrlOrPath))
+
+      // This service worker file is effectively a 'no-op' that will reset any
+      // previous service worker registered for the same host:port combination.
+      // We do this in development to avoid hitting the production cache if
+      // it used the same host and port.
+      // https://github.com/facebook/create-react-app/issues/2272#issuecomment-302832432
+      devServer.app.use(noopServiceWorkerMiddleware(paths.publicUrlOrPath))
+    }
+  }
+}
diff --git a/package-lock.json b/package-lock.json
index 84f4b4d..c47e941 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -8,51 +8,88 @@
       "name": "metriq",
       "version": "0.1.0",
       "dependencies": {
+        "@babel/core": "^7.16.0",
         "@babel/plugin-proposal-private-property-in-object": "^7.21.11",
         "@fortawesome/fontawesome-svg-core": "^1.2.35",
         "@fortawesome/free-solid-svg-icons": "^5.15.3",
         "@fortawesome/react-fontawesome": "^0.1.14",
+        "@json2csv/plainjs": "^7.0.6",
+        "@pmmmwh/react-refresh-webpack-plugin": "^0.5.3",
+        "@svgr/webpack": "^8.1.0",
         "@testing-library/jest-dom": "^5.12.0",
-        "axios": "^0.21.1",
+        "axios": "^1.6.8",
+        "babel-jest": "^27.4.2",
+        "babel-loader": "^8.2.3",
+        "babel-plugin-named-asset-import": "^0.3.8",
+        "babel-preset-react-app": "^10.0.1",
+        "bfj": "^7.0.2",
         "bootstrap": "^4.6.0",
+        "browserslist": "^4.18.1",
+        "camelcase": "^6.2.1",
+        "case-sensitive-paths-webpack-plugin": "^2.4.0",
         "chart.js": "3.8.0",
         "chartjs-adapter-moment": "^1.0.0",
         "chartjs-chart-error-bars": "3.8.0",
         "chartjs-plugin-annotation": "^2.2.1",
         "chartjs-plugin-datalabels": "^2.0.0",
+        "css-loader": "^6.5.1",
+        "css-minimizer-webpack-plugin": "^3.2.0",
         "d3": "^7.8.5",
-        "eslint-config-react-app": "^6.0.0",
+        "dotenv": "^10.0.0",
+        "dotenv-expand": "^5.1.0",
+        "eslint": "^8.3.0",
+        "eslint-config-react-app": "^7.0.1",
+        "eslint-webpack-plugin": "^3.1.1",
         "express": "^4.17.3",
+        "file-loader": "^6.2.0",
         "file-saver": "^2.0.5",
+        "fs-extra": "^10.0.0",
+        "html-webpack-plugin": "^5.5.0",
         "http-proxy-middleware": "^2.0.6",
-        "json2csv": "^5.0.7",
+        "identity-obj-proxy": "^3.0.0",
+        "jest": "^27.4.3",
+        "jest-resolve": "^27.4.2",
+        "jest-watch-typeahead": "^1.0.0",
         "katex": "^0.16.7",
+        "mini-css-extract-plugin": "^2.4.5",
         "moment": "^2.29.1",
+        "postcss": "^8.4.4",
+        "postcss-flexbugs-fixes": "^5.0.2",
+        "postcss-loader": "^6.2.1",
+        "postcss-normalize": "^10.0.1",
+        "postcss-preset-env": "^7.0.1",
+        "prompts": "^2.4.2",
         "prop-types": "^15.7.2",
         "react": "^17.0.2",
+        "react-app-polyfill": "^3.0.0",
         "react-bootstrap": "^1.6.0",
         "react-bootstrap-typeahead": "^5.2.0",
         "react-cookie-consent": "^8.0.1",
+        "react-dev-utils": "^12.0.1",
         "react-dom": "^17.0.2",
         "react-icons": "^4.3.1",
         "react-infinite-scroll-component": "^6.1.0",
         "react-katex": "^3.0.1",
         "react-mailchimp-subscribe": "^2.1.3",
+        "react-refresh": "^0.11.0",
         "react-router-dom": "^5.2.0",
-        "react-scripts": "^5.0.1",
         "react-share": "^4.4.0",
+        "resolve": "^1.20.0",
+        "resolve-url-loader": "^5.0.0",
+        "sass-loader": "^12.3.0",
+        "semver": "^7.3.5",
+        "source-map-loader": "^3.0.0",
+        "style-loader": "^3.3.1",
+        "tailwindcss": "^3.0.2",
+        "terser-webpack-plugin": "^5.2.5",
         "web-vitals": "^1.1.2",
+        "webpack": "^5.64.4",
+        "webpack-dev-server": "^4.6.0",
+        "webpack-manifest-plugin": "^4.0.2",
+        "workbox-webpack-plugin": "^6.4.1",
         "xmlserializer": "^0.6.1"
       }
     },
-    "node_modules/@aashutoshrathi/word-wrap": {
-      "version": "1.2.6",
-      "resolved": "https://registry.npmjs.org/@aashutoshrathi/word-wrap/-/word-wrap-1.2.6.tgz",
-      "integrity": "sha512-1Yjs2SvM8TflER/OD3cOjhWWOZb58A2t7wpE2S9XfBYTiIl+XFhQG2bjy4Pu1I+EAlCNUzRDYDdFwFYUKvXcIA==",
-      "engines": {
-        "node": ">=0.10.0"
-      }
-    },
     "node_modules/@adobe/css-tools": {
       "version": "4.3.3",
       "resolved": "https://registry.npmjs.org/@adobe/css-tools/-/css-tools-4.3.3.tgz",
@@ -102,20 +139,20 @@
       }
     },
     "node_modules/@babel/core": {
-      "version": "7.24.4",
-      "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.24.4.tgz",
-      "integrity": "sha512-MBVlMXP+kkl5394RBLSxxk/iLTeVGuXTV3cIDXavPpMMqnSnt6apKgan/U8O3USWZCWZT/TbgfEpKa4uMgN4Dg==",
+      "version": "7.24.5",
+      "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.24.5.tgz",
+      "integrity": "sha512-tVQRucExLQ02Boi4vdPp49svNGcfL2GhdTCT9aldhXgCJVAI21EtRfBettiuLUwce/7r6bFdgs6JFkcdTiFttA==",
       "dependencies": {
         "@ampproject/remapping": "^2.2.0",
         "@babel/code-frame": "^7.24.2",
-        "@babel/generator": "^7.24.4",
+        "@babel/generator": "^7.24.5",
         "@babel/helper-compilation-targets": "^7.23.6",
-        "@babel/helper-module-transforms": "^7.23.3",
-        "@babel/helpers": "^7.24.4",
-        "@babel/parser": "^7.24.4",
+        "@babel/helper-module-transforms": "^7.24.5",
+        "@babel/helpers": "^7.24.5",
+        "@babel/parser": "^7.24.5",
         "@babel/template": "^7.24.0",
-        "@babel/traverse": "^7.24.1",
-        "@babel/types": "^7.24.0",
+        "@babel/traverse": "^7.24.5",
+        "@babel/types": "^7.24.5",
         "convert-source-map": "^2.0.0",
         "debug": "^4.1.0",
         "gensync": "^1.0.0-beta.2",
@@ -130,10 +167,18 @@
         "url": "https://opencollective.com/babel"
       }
     },
+    "node_modules/@babel/core/node_modules/semver": {
+      "version": "6.3.1",
+      "resolved": "https://registry.npmjs.org/semver/-/semver-6.3.1.tgz",
+      "integrity": "sha512-BR7VvDCVHO+q2xBEWskxS6DJE1qRnb7DxzUrogb71CWoSficBxYsiAGd+Kl0mmq/MprG9yArRkyrQxTO6XjMzA==",
+      "bin": {
+        "semver": "bin/semver.js"
+      }
+    },
     "node_modules/@babel/eslint-parser": {
-      "version": "7.24.1",
-      "resolved": "https://registry.npmjs.org/@babel/eslint-parser/-/eslint-parser-7.24.1.tgz",
-      "integrity": "sha512-d5guuzMlPeDfZIbpQ8+g1NaCNuAGBBGNECh0HVqz1sjOeVLh2CEaifuOysCH18URW6R7pqXINvf5PaR/dC6jLQ==",
+      "version": "7.24.5",
+      "resolved": "https://registry.npmjs.org/@babel/eslint-parser/-/eslint-parser-7.24.5.tgz",
+      "integrity": "sha512-gsUcqS/fPlgAw1kOtpss7uhY6E9SFFANQ6EFX5GTvzUwaV0+sGaZWk6xq22MOdeT9wfxyokW3ceCUvOiRtZciQ==",
       "dependencies": {
         "@nicolo-ribaudo/eslint-scope-5-internals": "5.1.1-v1",
         "eslint-visitor-keys": "^2.1.0",
@@ -144,15 +189,31 @@
       },
       "peerDependencies": {
         "@babel/core": "^7.11.0",
-        "eslint": "^7.5.0 || ^8.0.0"
+        "eslint": "^7.5.0 || ^8.0.0 || ^9.0.0"
+      }
+    },
+    "node_modules/@babel/eslint-parser/node_modules/eslint-visitor-keys": {
+      "version": "2.1.0",
+      "resolved": "https://registry.npmjs.org/eslint-visitor-keys/-/eslint-visitor-keys-2.1.0.tgz",
+      "integrity": "sha512-0rSmRBzXgDzIsD6mGdJgevzgezI534Cer5L/vyMX0kHzT/jiB43jRhd9YUlMGYLQy2zprNmoT8qasCGtY+QaKw==",
+      "engines": {
+        "node": ">=10"
+      }
+    },
+    "node_modules/@babel/eslint-parser/node_modules/semver": {
+      "version": "6.3.1",
+      "resolved": "https://registry.npmjs.org/semver/-/semver-6.3.1.tgz",
+      "integrity": "sha512-BR7VvDCVHO+q2xBEWskxS6DJE1qRnb7DxzUrogb71CWoSficBxYsiAGd+Kl0mmq/MprG9yArRkyrQxTO6XjMzA==",
+      "bin": {
+        "semver": "bin/semver.js"
       }
     },
     "node_modules/@babel/generator": {
-      "version": "7.24.4",
-      "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.24.4.tgz",
-      "integrity": "sha512-Xd6+v6SnjWVx/nus+y0l1sxMOTOMBkyL4+BIdbALyatQnAe/SRVjANeDPSCYaX+i1iJmuGSKf3Z+E+V/va1Hvw==",
+      "version": "7.24.5",
+      "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.24.5.tgz",
+      "integrity": "sha512-x32i4hEXvr+iI0NEoEfDKzlemF8AmtOP8CcrRaEcpzysWuoEb1KknpcvMsHKPONoKZiDuItklgWhB18xEhr9PA==",
       "dependencies": {
-        "@babel/types": "^7.24.0",
+        "@babel/types": "^7.24.5",
         "@jridgewell/gen-mapping": "^0.3.5",
         "@jridgewell/trace-mapping": "^0.3.25",
         "jsesc": "^2.5.1"
@@ -198,19 +259,27 @@
         "node": ">=6.9.0"
       }
     },
+    "node_modules/@babel/helper-compilation-targets/node_modules/semver": {
+      "version": "6.3.1",
+      "resolved": "https://registry.npmjs.org/semver/-/semver-6.3.1.tgz",
+      "integrity": "sha512-BR7VvDCVHO+q2xBEWskxS6DJE1qRnb7DxzUrogb71CWoSficBxYsiAGd+Kl0mmq/MprG9yArRkyrQxTO6XjMzA==",
+      "bin": {
+        "semver": "bin/semver.js"
+      }
+    },
     "node_modules/@babel/helper-create-class-features-plugin": {
-      "version": "7.24.4",
-      "resolved": "https://registry.npmjs.org/@babel/helper-create-class-features-plugin/-/helper-create-class-features-plugin-7.24.4.tgz",
-      "integrity": "sha512-lG75yeuUSVu0pIcbhiYMXBXANHrpUPaOfu7ryAzskCgKUHuAxRQI5ssrtmF0X9UXldPlvT0XM/A4F44OXRt6iQ==",
+      "version": "7.24.5",
+      "resolved": "https://registry.npmjs.org/@babel/helper-create-class-features-plugin/-/helper-create-class-features-plugin-7.24.5.tgz",
+      "integrity": "sha512-uRc4Cv8UQWnE4NXlYTIIdM7wfFkOqlFztcC/gVXDKohKoVB3OyonfelUBaJzSwpBntZ2KYGF/9S7asCHsXwW6g==",
       "dependencies": {
         "@babel/helper-annotate-as-pure": "^7.22.5",
         "@babel/helper-environment-visitor": "^7.22.20",
         "@babel/helper-function-name": "^7.23.0",
-        "@babel/helper-member-expression-to-functions": "^7.23.0",
+        "@babel/helper-member-expression-to-functions": "^7.24.5",
         "@babel/helper-optimise-call-expression": "^7.22.5",
         "@babel/helper-replace-supers": "^7.24.1",
         "@babel/helper-skip-transparent-expression-wrappers": "^7.22.5",
-        "@babel/helper-split-export-declaration": "^7.22.6",
+        "@babel/helper-split-export-declaration": "^7.24.5",
         "semver": "^6.3.1"
       },
       "engines": {
@@ -220,6 +289,14 @@
         "@babel/core": "^7.0.0"
       }
     },
+    "node_modules/@babel/helper-create-class-features-plugin/node_modules/semver": {
+      "version": "6.3.1",
+      "resolved": "https://registry.npmjs.org/semver/-/semver-6.3.1.tgz",
+      "integrity": "sha512-BR7VvDCVHO+q2xBEWskxS6DJE1qRnb7DxzUrogb71CWoSficBxYsiAGd+Kl0mmq/MprG9yArRkyrQxTO6XjMzA==",
+      "bin": {
+        "semver": "bin/semver.js"
+      }
+    },
     "node_modules/@babel/helper-create-regexp-features-plugin": {
       "version": "7.22.15",
       "resolved": "https://registry.npmjs.org/@babel/helper-create-regexp-features-plugin/-/helper-create-regexp-features-plugin-7.22.15.tgz",
@@ -236,6 +313,14 @@
         "@babel/core": "^7.0.0"
       }
     },
+    "node_modules/@babel/helper-create-regexp-features-plugin/node_modules/semver": {
+      "version": "6.3.1",
+      "resolved": "https://registry.npmjs.org/semver/-/semver-6.3.1.tgz",
+      "integrity": "sha512-BR7VvDCVHO+q2xBEWskxS6DJE1qRnb7DxzUrogb71CWoSficBxYsiAGd+Kl0mmq/MprG9yArRkyrQxTO6XjMzA==",
+      "bin": {
+        "semver": "bin/semver.js"
+      }
+    },
     "node_modules/@babel/helper-define-polyfill-provider": {
       "version": "0.6.2",
       "resolved": "https://registry.npmjs.org/@babel/helper-define-polyfill-provider/-/helper-define-polyfill-provider-0.6.2.tgz",
@@ -283,11 +368,11 @@
       }
     },
     "node_modules/@babel/helper-member-expression-to-functions": {
-      "version": "7.23.0",
-      "resolved": "https://registry.npmjs.org/@babel/helper-member-expression-to-functions/-/helper-member-expression-to-functions-7.23.0.tgz",
-      "integrity": "sha512-6gfrPwh7OuT6gZyJZvd6WbTfrqAo7vm4xCzAXOusKqq/vWdKXphTpj5klHKNmRUU6/QRGlBsyU9mAIPaWHlqJA==",
+      "version": "7.24.5",
+      "resolved": "https://registry.npmjs.org/@babel/helper-member-expression-to-functions/-/helper-member-expression-to-functions-7.24.5.tgz",
+      "integrity": "sha512-4owRteeihKWKamtqg4JmWSsEZU445xpFRXPEwp44HbgbxdWlUV1b4Agg4lkA806Lil5XM/e+FJyS0vj5T6vmcA==",
       "dependencies": {
-        "@babel/types": "^7.23.0"
+        "@babel/types": "^7.24.5"
       },
       "engines": {
         "node": ">=6.9.0"
@@ -305,15 +390,15 @@
       }
     },
     "node_modules/@babel/helper-module-transforms": {
-      "version": "7.23.3",
-      "resolved": "https://registry.npmjs.org/@babel/helper-module-transforms/-/helper-module-transforms-7.23.3.tgz",
-      "integrity": "sha512-7bBs4ED9OmswdfDzpz4MpWgSrV7FXlc3zIagvLFjS5H+Mk7Snr21vQ6QwrsoCGMfNC4e4LQPdoULEt4ykz0SRQ==",
+      "version": "7.24.5",
+      "resolved": "https://registry.npmjs.org/@babel/helper-module-transforms/-/helper-module-transforms-7.24.5.tgz",
+      "integrity": "sha512-9GxeY8c2d2mdQUP1Dye0ks3VDyIMS98kt/llQ2nUId8IsWqTF0l1LkSX0/uP7l7MCDrzXS009Hyhe2gzTiGW8A==",
       "dependencies": {
         "@babel/helper-environment-visitor": "^7.22.20",
-        "@babel/helper-module-imports": "^7.22.15",
-        "@babel/helper-simple-access": "^7.22.5",
-        "@babel/helper-split-export-declaration": "^7.22.6",
-        "@babel/helper-validator-identifier": "^7.22.20"
+        "@babel/helper-module-imports": "^7.24.3",
+        "@babel/helper-simple-access": "^7.24.5",
+        "@babel/helper-split-export-declaration": "^7.24.5",
+        "@babel/helper-validator-identifier": "^7.24.5"
       },
       "engines": {
         "node": ">=6.9.0"
@@ -334,9 +419,9 @@
       }
     },
     "node_modules/@babel/helper-plugin-utils": {
-      "version": "7.24.0",
-      "resolved": "https://registry.npmjs.org/@babel/helper-plugin-utils/-/helper-plugin-utils-7.24.0.tgz",
-      "integrity": "sha512-9cUznXMG0+FxRuJfvL82QlTqIzhVW9sL0KjMPHhAOOvpQGL8QtdxnBKILjBqxlHyliz0yCa1G903ZXI/FuHy2w==",
+      "version": "7.24.5",
+      "resolved": "https://registry.npmjs.org/@babel/helper-plugin-utils/-/helper-plugin-utils-7.24.5.tgz",
+      "integrity": "sha512-xjNLDopRzW2o6ba0gKbkZq5YWEBaK3PCyTOY1K2P/O07LGMhMqlMXPxwN4S5/RhWuCobT8z0jrlKGlYmeR1OhQ==",
       "engines": {
         "node": ">=6.9.0"
       }
@@ -374,11 +459,11 @@
       }
     },
     "node_modules/@babel/helper-simple-access": {
-      "version": "7.22.5",
-      "resolved": "https://registry.npmjs.org/@babel/helper-simple-access/-/helper-simple-access-7.22.5.tgz",
-      "integrity": "sha512-n0H99E/K+Bika3++WNL17POvo4rKWZ7lZEp1Q+fStVbUi8nxPQEBOlTmCOxW/0JsS56SKKQ+ojAe2pHKJHN35w==",
+      "version": "7.24.5",
+      "resolved": "https://registry.npmjs.org/@babel/helper-simple-access/-/helper-simple-access-7.24.5.tgz",
+      "integrity": "sha512-uH3Hmf5q5n7n8mz7arjUlDOCbttY/DW4DYhE6FUsjKJ/oYC1kQQUvwEQWxRwUpX9qQKRXeqLwWxrqilMrf32sQ==",
       "dependencies": {
-        "@babel/types": "^7.22.5"
+        "@babel/types": "^7.24.5"
       },
       "engines": {
         "node": ">=6.9.0"
@@ -396,11 +481,11 @@
       }
     },
     "node_modules/@babel/helper-split-export-declaration": {
-      "version": "7.22.6",
-      "resolved": "https://registry.npmjs.org/@babel/helper-split-export-declaration/-/helper-split-export-declaration-7.22.6.tgz",
-      "integrity": "sha512-AsUnxuLhRYsisFiaJwvp1QF+I3KjD5FOxut14q/GzovUe6orHLesW2C7d754kRm53h5gqrz6sFl6sxc4BVtE/g==",
+      "version": "7.24.5",
+      "resolved": "https://registry.npmjs.org/@babel/helper-split-export-declaration/-/helper-split-export-declaration-7.24.5.tgz",
+      "integrity": "sha512-5CHncttXohrHk8GWOFCcCl4oRD9fKosWlIRgWm4ql9VYioKm52Mk2xsmoohvm7f3JoiLSM5ZgJuRaf5QZZYd3Q==",
       "dependencies": {
-        "@babel/types": "^7.22.5"
+        "@babel/types": "^7.24.5"
       },
       "engines": {
         "node": ">=6.9.0"
@@ -415,9 +500,9 @@
       }
     },
     "node_modules/@babel/helper-validator-identifier": {
-      "version": "7.22.20",
-      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.22.20.tgz",
-      "integrity": "sha512-Y4OZ+ytlatR8AI+8KZfKuL5urKp7qey08ha31L8b3BwewJAoJamTzyvxPR/5D+KkdJCGPq/+8TukHBlY10FX9A==",
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-      "resolved": "https://registry.npmjs.org/tough-cookie/-/tough-cookie-4.1.3.tgz",
-      "integrity": "sha512-aX/y5pVRkfRnfmuX+OdbSdXvPe6ieKX/G2s7e98f4poJHnqH3281gDPm/metm6E/WRamfx7WC4HUqkWHfQHprw==",
+      "version": "4.1.4",
+      "resolved": "https://registry.npmjs.org/tough-cookie/-/tough-cookie-4.1.4.tgz",
+      "integrity": "sha512-Loo5UUvLD9ScZ6jh8beX1T6sO1w2/MpCRpEP7V280GKMVUQ0Jzar2U3UJPsrdbziLEMMhu3Ujnq//rhiFuIeag==",
       "dependencies": {
         "psl": "^1.1.33",
         "punycode": "^2.1.1",
@@ -21132,16 +18815,16 @@
       }
     },
     "node_modules/typescript": {
-      "version": "4.9.5",
-      "resolved": "https://registry.npmjs.org/typescript/-/typescript-4.9.5.tgz",
-      "integrity": "sha512-1FXk9E2Hm+QzZQ7z+McJiHL4NW1F2EzMu9Nq9i3zAaGqibafqYwCVU6WyWAuyQRRzOlxou8xZSyXLEN8oKj24g==",
+      "version": "5.4.5",
+      "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.4.5.tgz",
+      "integrity": "sha512-vcI4UpRgg81oIRUFwR0WSIHKt11nJ7SAVlYNIu+QpqeyXP+gpQJy/Z4+F0aGxSE4MqwjyXvW/TzgkLAx2AGHwQ==",
       "peer": true,
       "bin": {
         "tsc": "bin/tsc",
         "tsserver": "bin/tsserver"
       },
       "engines": {
-        "node": ">=4.2.0"
+        "node": ">=14.17"
       }
     },
     "node_modules/unbox-primitive": {
@@ -21245,11 +18928,6 @@
         "node": ">= 0.8"
       }
     },
-    "node_modules/unquote": {
-      "version": "1.1.1",
-      "resolved": "https://registry.npmjs.org/unquote/-/unquote-1.1.1.tgz",
-      "integrity": "sha512-vRCqFv6UhXpWxZPyGDh/F3ZpNv8/qo7w6iufLpQg9aKnQ71qM4B5KiI7Mia9COcjEhrO9LueHpMYjYzsWH3OIg=="
-    },
     "node_modules/upath": {
       "version": "1.2.0",
       "resolved": "https://registry.npmjs.org/upath/-/upath-1.2.0.tgz",
@@ -21260,9 +18938,9 @@
       }
     },
     "node_modules/update-browserslist-db": {
-      "version": "1.0.13",
-      "resolved": "https://registry.npmjs.org/update-browserslist-db/-/update-browserslist-db-1.0.13.tgz",
-      "integrity": "sha512-xebP81SNcPuNpPP3uzeW1NYXxI3rxyJzF3pD6sH4jE7o/IX+WtSpwnVU+qIsDPyk0d3hmFQ7mjqc6AtV604hbg==",
+      "version": "1.0.15",
+      "resolved": "https://registry.npmjs.org/update-browserslist-db/-/update-browserslist-db-1.0.15.tgz",
+      "integrity": "sha512-K9HWH62x3/EalU1U6sjSZiylm9C8tgq2mSvshZpqc7QE69RaA2qjhkW2HlNA0tFpEbtyFz7HTqbSdN4MSwUodA==",
       "funding": [
         {
           "type": "opencollective",
@@ -21278,7 +18956,7 @@
         }
       ],
       "dependencies": {
-        "escalade": "^3.1.1",
+        "escalade": "^3.1.2",
         "picocolors": "^1.0.0"
       },
       "bin": {
@@ -21310,20 +18988,6 @@
       "resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz",
       "integrity": "sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw=="
     },
-    "node_modules/util.promisify": {
-      "version": "1.0.1",
-      "resolved": "https://registry.npmjs.org/util.promisify/-/util.promisify-1.0.1.tgz",
-      "integrity": "sha512-g9JpC/3He3bm38zsLupWryXHoEcS22YHthuPQSJdMy6KNrzIRzWqcsHzD/WUnqe45whVou4VIsPew37DoXWNrA==",
-      "dependencies": {
-        "define-properties": "^1.1.3",
-        "es-abstract": "^1.17.2",
-        "has-symbols": "^1.0.1",
-        "object.getownpropertydescriptors": "^2.1.0"
-      },
-      "funding": {
-        "url": "https://github.com/sponsors/ljharb"
-      }
-    },
     "node_modules/utila": {
       "version": "0.4.0",
       "resolved": "https://registry.npmjs.org/utila/-/utila-0.4.0.tgz",
@@ -21345,12 +19009,6 @@
         "uuid": "dist/bin/uuid"
       }
     },
-    "node_modules/v8-compile-cache": {
-      "version": "2.4.0",
-      "resolved": "https://registry.npmjs.org/v8-compile-cache/-/v8-compile-cache-2.4.0.tgz",
-      "integrity": "sha512-ocyWc3bAHBB/guyqJQVI5o4BZkPhznPYUG2ea80Gond/BgNWpap8TOmLSeeQG7bnh2KMISxskdADG59j7zruhw==",
-      "peer": true
-    },
     "node_modules/v8-to-istanbul": {
       "version": "8.1.1",
       "resolved": "https://registry.npmjs.org/v8-to-istanbul/-/v8-to-istanbul-8.1.1.tgz",
@@ -21520,14 +19178,14 @@
       }
     },
     "node_modules/webpack-dev-middleware/node_modules/ajv": {
-      "version": "8.12.0",
-      "resolved": "https://registry.npmjs.org/ajv/-/ajv-8.12.0.tgz",
-      "integrity": "sha512-sRu1kpcO9yLtYxBKvqfTeh9KzZEwO3STyX1HT+4CaDzC6HpTGYhIhPIzj9XuKU7KYDwnaeh5hcOwjy1QuJzBPA==",
+      "version": "8.13.0",
+      "resolved": "https://registry.npmjs.org/ajv/-/ajv-8.13.0.tgz",
+      "integrity": "sha512-PRA911Blj99jR5RMeTunVbNXMF6Lp4vZXnk5GQjcnUWUTsrXtekg/pnmFFI2u/I36Y/2bITGS30GZCXei6uNkA==",
       "dependencies": {
-        "fast-deep-equal": "^3.1.1",
+        "fast-deep-equal": "^3.1.3",
         "json-schema-traverse": "^1.0.0",
         "require-from-string": "^2.0.2",
-        "uri-js": "^4.2.2"
+        "uri-js": "^4.4.1"
       },
       "funding": {
         "type": "github",
@@ -21627,14 +19285,14 @@
       }
     },
     "node_modules/webpack-dev-server/node_modules/ajv": {
-      "version": "8.12.0",
-      "resolved": "https://registry.npmjs.org/ajv/-/ajv-8.12.0.tgz",
-      "integrity": "sha512-sRu1kpcO9yLtYxBKvqfTeh9KzZEwO3STyX1HT+4CaDzC6HpTGYhIhPIzj9XuKU7KYDwnaeh5hcOwjy1QuJzBPA==",
+      "version": "8.13.0",
+      "resolved": "https://registry.npmjs.org/ajv/-/ajv-8.13.0.tgz",
+      "integrity": "sha512-PRA911Blj99jR5RMeTunVbNXMF6Lp4vZXnk5GQjcnUWUTsrXtekg/pnmFFI2u/I36Y/2bITGS30GZCXei6uNkA==",
       "dependencies": {
-        "fast-deep-equal": "^3.1.1",
+        "fast-deep-equal": "^3.1.3",
         "json-schema-traverse": "^1.0.0",
         "require-from-string": "^2.0.2",
-        "uri-js": "^4.2.2"
+        "uri-js": "^4.4.1"
       },
       "funding": {
         "type": "github",
@@ -21684,9 +19342,9 @@
       }
     },
     "node_modules/webpack-dev-server/node_modules/ws": {
-      "version": "8.16.0",
-      "resolved": "https://registry.npmjs.org/ws/-/ws-8.16.0.tgz",
-      "integrity": "sha512-HS0c//TP7Ina87TfiPUz1rQzMhHrl/SG2guqRcTOIUYD2q8uhUdNHZYJUaQ8aTGPzCh+c6oawMKW35nFl1dxyQ==",
+      "version": "8.17.0",
+      "resolved": "https://registry.npmjs.org/ws/-/ws-8.17.0.tgz",
+      "integrity": "sha512-uJq6108EgZMAl20KagGkzCKfMEjxmKvZHG7Tlq0Z6nOky7YF7aq4mOx6xK8TJ/i1LeK4Qus7INktacctDgY8Ow==",
       "engines": {
         "node": ">=10.0.0"
       },
@@ -21746,23 +19404,24 @@
         "node": ">=10.13.0"
       }
     },
-    "node_modules/webpack/node_modules/acorn": {
-      "version": "8.11.3",
-      "resolved": "https://registry.npmjs.org/acorn/-/acorn-8.11.3.tgz",
-      "integrity": "sha512-Y9rRfJG5jcKOE0CLisYbojUjIrIEE7AGMzA/Sm4BslANhbS+cDMpgBdcPT91oJ7OuJ9hYJBx59RjbhxVnrF8Xg==",
-      "bin": {
-        "acorn": "bin/acorn"
+    "node_modules/webpack/node_modules/eslint-scope": {
+      "version": "5.1.1",
+      "resolved": "https://registry.npmjs.org/eslint-scope/-/eslint-scope-5.1.1.tgz",
+      "integrity": "sha512-2NxwbF/hZ0KpepYN0cNbo+FN6XoK7GaHlQhgx/hIZl6Va0bF45RQOOwhLIy8lQDbuCiadSLCBnH2CFYquit5bw==",
+      "dependencies": {
+        "esrecurse": "^4.3.0",
+        "estraverse": "^4.1.1"
       },
       "engines": {
-        "node": ">=0.4.0"
+        "node": ">=8.0.0"
       }
     },
-    "node_modules/webpack/node_modules/acorn-import-assertions": {
-      "version": "1.9.0",
-      "resolved": "https://registry.npmjs.org/acorn-import-assertions/-/acorn-import-assertions-1.9.0.tgz",
-      "integrity": "sha512-cmMwop9x+8KFhxvKrKfPYmN6/pKTYYHBqLa0DfvVZcKMJWNyWLnaqND7dx/qn66R7ewM1UX5XMaDVP5wlVTaVA==",
-      "peerDependencies": {
-        "acorn": "^8"
+    "node_modules/webpack/node_modules/estraverse": {
+      "version": "4.3.0",
+      "resolved": "https://registry.npmjs.org/estraverse/-/estraverse-4.3.0.tgz",
+      "integrity": "sha512-39nnKffWz8xN1BU/2c79n9nB9HDzo0niYUqx6xyqUnyoAnQyyWpOTdZEeiCch8BBu515t4wp9ZmgVfVhn9EBpw==",
+      "engines": {
+        "node": ">=4.0"
       }
     },
     "node_modules/websocket-driver": {
@@ -22006,14 +19665,14 @@
       }
     },
     "node_modules/workbox-build/node_modules/ajv": {
-      "version": "8.12.0",
-      "resolved": "https://registry.npmjs.org/ajv/-/ajv-8.12.0.tgz",
-      "integrity": "sha512-sRu1kpcO9yLtYxBKvqfTeh9KzZEwO3STyX1HT+4CaDzC6HpTGYhIhPIzj9XuKU7KYDwnaeh5hcOwjy1QuJzBPA==",
+      "version": "8.13.0",
+      "resolved": "https://registry.npmjs.org/ajv/-/ajv-8.13.0.tgz",
+      "integrity": "sha512-PRA911Blj99jR5RMeTunVbNXMF6Lp4vZXnk5GQjcnUWUTsrXtekg/pnmFFI2u/I36Y/2bITGS30GZCXei6uNkA==",
       "dependencies": {
-        "fast-deep-equal": "^3.1.1",
+        "fast-deep-equal": "^3.1.3",
         "json-schema-traverse": "^1.0.0",
         "require-from-string": "^2.0.2",
-        "uri-js": "^4.2.2"
+        "uri-js": "^4.4.1"
       },
       "funding": {
         "type": "github",
diff --git a/package.json b/package.json
index 59a3489..fe4d79c 100644
--- a/package.json
+++ b/package.json
@@ -3,48 +3,92 @@
   "version": "0.1.0",
   "private": true,
   "dependencies": {
+    "@babel/core": "^7.16.0",
     "@babel/plugin-proposal-private-property-in-object": "^7.21.11",
     "@fortawesome/fontawesome-svg-core": "^1.2.35",
     "@fortawesome/free-solid-svg-icons": "^5.15.3",
     "@fortawesome/react-fontawesome": "^0.1.14",
+    "@json2csv/plainjs": "^7.0.6",
+    "@pmmmwh/react-refresh-webpack-plugin": "^0.5.3",
+    "@svgr/webpack": "^8.1.0",
     "@testing-library/jest-dom": "^5.12.0",
-    "axios": "^0.21.1",
+    "axios": "^1.6.8",
+    "babel-jest": "^27.4.2",
+    "babel-loader": "^8.2.3",
+    "babel-plugin-named-asset-import": "^0.3.8",
+    "babel-preset-react-app": "^10.0.1",
+    "bfj": "^7.0.2",
     "bootstrap": "^4.6.0",
+    "browserslist": "^4.18.1",
+    "camelcase": "^6.2.1",
+    "case-sensitive-paths-webpack-plugin": "^2.4.0",
     "chart.js": "3.8.0",
     "chartjs-adapter-moment": "^1.0.0",
     "chartjs-chart-error-bars": "3.8.0",
     "chartjs-plugin-annotation": "^2.2.1",
     "chartjs-plugin-datalabels": "^2.0.0",
+    "css-loader": "^6.5.1",
+    "css-minimizer-webpack-plugin": "^3.2.0",
     "d3": "^7.8.5",
-    "eslint-config-react-app": "^6.0.0",
+    "dotenv": "^10.0.0",
+    "dotenv-expand": "^5.1.0",
+    "eslint": "^8.3.0",
+    "eslint-config-react-app": "^7.0.1",
+    "eslint-webpack-plugin": "^3.1.1",
     "express": "^4.17.3",
+    "file-loader": "^6.2.0",
     "file-saver": "^2.0.5",
+    "fs-extra": "^10.0.0",
+    "html-webpack-plugin": "^5.5.0",
     "http-proxy-middleware": "^2.0.6",
-    "json2csv": "^5.0.7",
+    "identity-obj-proxy": "^3.0.0",
+    "jest": "^27.4.3",
+    "jest-resolve": "^27.4.2",
+    "jest-watch-typeahead": "^1.0.0",
     "katex": "^0.16.7",
+    "mini-css-extract-plugin": "^2.4.5",
     "moment": "^2.29.1",
+    "postcss": "^8.4.4",
+    "postcss-flexbugs-fixes": "^5.0.2",
+    "postcss-loader": "^6.2.1",
+    "postcss-normalize": "^10.0.1",
+    "postcss-preset-env": "^7.0.1",
+    "prompts": "^2.4.2",
     "prop-types": "^15.7.2",
     "react": "^17.0.2",
+    "react-app-polyfill": "^3.0.0",
     "react-bootstrap": "^1.6.0",
     "react-bootstrap-typeahead": "^5.2.0",
     "react-cookie-consent": "^8.0.1",
+    "react-dev-utils": "^12.0.1",
     "react-dom": "^17.0.2",
     "react-icons": "^4.3.1",
     "react-infinite-scroll-component": "^6.1.0",
     "react-katex": "^3.0.1",
     "react-mailchimp-subscribe": "^2.1.3",
+    "react-refresh": "^0.11.0",
     "react-router-dom": "^5.2.0",
-    "react-scripts": "^5.0.1",
     "react-share": "^4.4.0",
+    "resolve": "^1.20.0",
+    "resolve-url-loader": "^5.0.0",
+    "sass-loader": "^12.3.0",
+    "semver": "^7.3.5",
+    "source-map-loader": "^3.0.0",
+    "style-loader": "^3.3.1",
+    "tailwindcss": "^3.0.2",
+    "terser-webpack-plugin": "^5.2.5",
     "web-vitals": "^1.1.2",
+    "webpack": "^5.64.4",
+    "webpack-dev-server": "^4.6.0",
+    "webpack-manifest-plugin": "^4.0.2",
+    "workbox-webpack-plugin": "^6.4.1",
     "xmlserializer": "^0.6.1"
   },
   "proxy": "http://localhost:8080",
   "scripts": {
-    "start": "react-scripts start",
-    "build": "react-scripts build",
-    "test": "react-scripts test",
-    "eject": "react-scripts eject",
+    "start": "node scripts/start.js",
+    "build": "node scripts/build.js",
+    "test": "node scripts/test.js",
     "server": "npm run build && node server/index.js"
   },
   "eslintConfig": {
@@ -64,5 +108,61 @@
       "last 1 firefox version",
       "last 1 safari version"
     ]
+  },
+  "jest": {
+    "roots": [
+      "<rootDir>/src"
+    ],
+    "collectCoverageFrom": [
+      "src/**/*.{js,jsx,ts,tsx}",
+      "!src/**/*.d.ts"
+    ],
+    "setupFiles": [
+      "react-app-polyfill/jsdom"
+    ],
+    "setupFilesAfterEnv": [
+      "<rootDir>/src/setupTests.js"
+    ],
+    "testMatch": [
+      "<rootDir>/src/**/__tests__/**/*.{js,jsx,ts,tsx}",
+      "<rootDir>/src/**/*.{spec,test}.{js,jsx,ts,tsx}"
+    ],
+    "testEnvironment": "jsdom",
+    "transform": {
+      "^.+\\.(js|jsx|mjs|cjs|ts|tsx)$": "<rootDir>/config/jest/babelTransform.js",
+      "^.+\\.css$": "<rootDir>/config/jest/cssTransform.js",
+      "^(?!.*\\.(js|jsx|mjs|cjs|ts|tsx|css|json)$)": "<rootDir>/config/jest/fileTransform.js"
+    },
+    "transformIgnorePatterns": [
+      "[/\\\\]node_modules[/\\\\].+\\.(js|jsx|mjs|cjs|ts|tsx)$",
+      "^.+\\.module\\.(css|sass|scss)$"
+    ],
+    "modulePaths": [],
+    "moduleNameMapper": {
+      "^react-native$": "react-native-web",
+      "^.+\\.module\\.(css|sass|scss)$": "identity-obj-proxy"
+    },
+    "moduleFileExtensions": [
+      "web.js",
+      "js",
+      "web.ts",
+      "ts",
+      "web.tsx",
+      "tsx",
+      "json",
+      "web.jsx",
+      "jsx",
+      "node"
+    ],
+    "watchPlugins": [
+      "jest-watch-typeahead/filename",
+      "jest-watch-typeahead/testname"
+    ],
+    "resetMocks": true
+  },
+  "babel": {
+    "presets": [
+      "react-app"
+    ]
   }
 }
diff --git a/scripts/build.js b/scripts/build.js
new file mode 100644
index 0000000..df3c4d6
--- /dev/null
+++ b/scripts/build.js
@@ -0,0 +1,217 @@
+'use strict'
+
+// Do this as the first thing so that any code reading it knows the right env.
+process.env.BABEL_ENV = 'production'
+process.env.NODE_ENV = 'production'
+
+// Makes the script crash on unhandled rejections instead of silently
+// ignoring them. In the future, promise rejections that are not handled will
+// terminate the Node.js process with a non-zero exit code.
+process.on('unhandledRejection', err => {
+  throw err
+})
+
+// Ensure environment variables are read.
+require('../config/env')
+
+const path = require('path')
+const chalk = require('react-dev-utils/chalk')
+const fs = require('fs-extra')
+const bfj = require('bfj')
+const webpack = require('webpack')
+const configFactory = require('../config/webpack.config')
+const paths = require('../config/paths')
+const checkRequiredFiles = require('react-dev-utils/checkRequiredFiles')
+const formatWebpackMessages = require('react-dev-utils/formatWebpackMessages')
+const printHostingInstructions = require('react-dev-utils/printHostingInstructions')
+const FileSizeReporter = require('react-dev-utils/FileSizeReporter')
+const printBuildError = require('react-dev-utils/printBuildError')
+
+const measureFileSizesBeforeBuild =
+  FileSizeReporter.measureFileSizesBeforeBuild
+const printFileSizesAfterBuild = FileSizeReporter.printFileSizesAfterBuild
+const useYarn = fs.existsSync(paths.yarnLockFile)
+
+// These sizes are pretty large. We'll warn for bundles exceeding them.
+const WARN_AFTER_BUNDLE_GZIP_SIZE = 512 * 1024
+const WARN_AFTER_CHUNK_GZIP_SIZE = 1024 * 1024
+
+const isInteractive = process.stdout.isTTY
+
+// Warn and crash if required files are missing
+if (!checkRequiredFiles([paths.appHtml, paths.appIndexJs])) {
+  process.exit(1)
+}
+
+const argv = process.argv.slice(2)
+const writeStatsJson = argv.indexOf('--stats') !== -1
+
+// Generate configuration
+const config = configFactory('production')
+
+// We require that you explicitly set browsers and do not fall back to
+// browserslist defaults.
+const { checkBrowsers } = require('react-dev-utils/browsersHelper')
+checkBrowsers(paths.appPath, isInteractive)
+  .then(() => {
+    // First, read the current file sizes in build directory.
+    // This lets us display how much they changed later.
+    return measureFileSizesBeforeBuild(paths.appBuild)
+  })
+  .then(previousFileSizes => {
+    // Remove all content but keep the directory so that
+    // if you're in it, you don't end up in Trash
+    fs.emptyDirSync(paths.appBuild)
+    // Merge with the public folder
+    copyPublicFolder()
+    // Start the webpack build
+    return build(previousFileSizes)
+  })
+  .then(
+    ({ stats, previousFileSizes, warnings }) => {
+      if (warnings.length) {
+        console.log(chalk.yellow('Compiled with warnings.\n'))
+        console.log(warnings.join('\n\n'))
+        console.log(
+          '\nSearch for the ' +
+            chalk.underline(chalk.yellow('keywords')) +
+            ' to learn more about each warning.'
+        )
+        console.log(
+          'To ignore, add ' +
+            chalk.cyan('// eslint-disable-next-line') +
+            ' to the line before.\n'
+        )
+      } else {
+        console.log(chalk.green('Compiled successfully.\n'))
+      }
+
+      console.log('File sizes after gzip:\n')
+      printFileSizesAfterBuild(
+        stats,
+        previousFileSizes,
+        paths.appBuild,
+        WARN_AFTER_BUNDLE_GZIP_SIZE,
+        WARN_AFTER_CHUNK_GZIP_SIZE
+      )
+      console.log()
+
+      const appPackage = require(paths.appPackageJson)
+      const publicUrl = paths.publicUrlOrPath
+      const publicPath = config.output.publicPath
+      const buildFolder = path.relative(process.cwd(), paths.appBuild)
+      printHostingInstructions(
+        appPackage,
+        publicUrl,
+        publicPath,
+        buildFolder,
+        useYarn
+      )
+    },
+    err => {
+      const tscCompileOnError = process.env.TSC_COMPILE_ON_ERROR === 'true'
+      if (tscCompileOnError) {
+        console.log(
+          chalk.yellow(
+            'Compiled with the following type errors (you may want to check these before deploying your app):\n'
+          )
+        )
+        printBuildError(err)
+      } else {
+        console.log(chalk.red('Failed to compile.\n'))
+        printBuildError(err)
+        process.exit(1)
+      }
+    }
+  )
+  .catch(err => {
+    if (err && err.message) {
+      console.log(err.message)
+    }
+    process.exit(1)
+  })
+
+// Create the production build and print the deployment instructions.
+function build (previousFileSizes) {
+  console.log('Creating an optimized production build...')
+
+  const compiler = webpack(config)
+  return new Promise((resolve, reject) => {
+    compiler.run((err, stats) => {
+      let messages
+      if (err) {
+        if (!err.message) {
+          return reject(err)
+        }
+
+        let errMessage = err.message
+
+        // Add additional information for postcss errors
+        if (Object.prototype.hasOwnProperty.call(err, 'postcssNode')) {
+          errMessage +=
+            '\nCompileError: Begins at CSS selector ' +
+            err.postcssNode.selector
+        }
+
+        messages = formatWebpackMessages({
+          errors: [errMessage],
+          warnings: []
+        })
+      } else {
+        messages = formatWebpackMessages(
+          stats.toJson({ all: false, warnings: true, errors: true })
+        )
+      }
+      if (messages.errors.length) {
+        // Only keep the first error. Others are often indicative
+        // of the same problem, but confuse the reader with noise.
+        if (messages.errors.length > 1) {
+          messages.errors.length = 1
+        }
+        return reject(new Error(messages.errors.join('\n\n')))
+      }
+      if (
+        process.env.CI &&
+        (typeof process.env.CI !== 'string' ||
+          process.env.CI.toLowerCase() !== 'false') &&
+        messages.warnings.length
+      ) {
+        // Ignore sourcemap warnings in CI builds. See #8227 for more info.
+        const filteredWarnings = messages.warnings.filter(
+          w => !/Failed to parse source map/.test(w)
+        )
+        if (filteredWarnings.length) {
+          console.log(
+            chalk.yellow(
+              '\nTreating warnings as errors because process.env.CI = true.\n' +
+                'Most CI servers set it automatically.\n'
+            )
+          )
+          return reject(new Error(filteredWarnings.join('\n\n')))
+        }
+      }
+
+      const resolveArgs = {
+        stats,
+        previousFileSizes,
+        warnings: messages.warnings
+      }
+
+      if (writeStatsJson) {
+        return bfj
+          .write(paths.appBuild + '/bundle-stats.json', stats.toJson())
+          .then(() => resolve(resolveArgs))
+          .catch(error => reject(new Error(error)))
+      }
+
+      return resolve(resolveArgs)
+    })
+  })
+}
+
+function copyPublicFolder () {
+  fs.copySync(paths.appPublic, paths.appBuild, {
+    dereference: true,
+    filter: file => file !== paths.appHtml
+  })
+}
diff --git a/scripts/start.js b/scripts/start.js
new file mode 100644
index 0000000..4821097
--- /dev/null
+++ b/scripts/start.js
@@ -0,0 +1,154 @@
+'use strict'
+
+// Do this as the first thing so that any code reading it knows the right env.
+process.env.BABEL_ENV = 'development'
+process.env.NODE_ENV = 'development'
+
+// Makes the script crash on unhandled rejections instead of silently
+// ignoring them. In the future, promise rejections that are not handled will
+// terminate the Node.js process with a non-zero exit code.
+process.on('unhandledRejection', err => {
+  throw err
+})
+
+// Ensure environment variables are read.
+require('../config/env')
+
+const fs = require('fs')
+const chalk = require('react-dev-utils/chalk')
+const webpack = require('webpack')
+const WebpackDevServer = require('webpack-dev-server')
+const clearConsole = require('react-dev-utils/clearConsole')
+const checkRequiredFiles = require('react-dev-utils/checkRequiredFiles')
+const {
+  choosePort,
+  createCompiler,
+  prepareProxy,
+  prepareUrls
+} = require('react-dev-utils/WebpackDevServerUtils')
+const openBrowser = require('react-dev-utils/openBrowser')
+const semver = require('semver')
+const paths = require('../config/paths')
+const configFactory = require('../config/webpack.config')
+const createDevServerConfig = require('../config/webpackDevServer.config')
+const getClientEnvironment = require('../config/env')
+const react = require(require.resolve('react', { paths: [paths.appPath] }))
+
+const env = getClientEnvironment(paths.publicUrlOrPath.slice(0, -1))
+const useYarn = fs.existsSync(paths.yarnLockFile)
+const isInteractive = process.stdout.isTTY
+
+// Warn and crash if required files are missing
+if (!checkRequiredFiles([paths.appHtml, paths.appIndexJs])) {
+  process.exit(1)
+}
+
+// Tools like Cloud9 rely on this.
+const DEFAULT_PORT = parseInt(process.env.PORT, 10) || 3000
+const HOST = process.env.HOST || '0.0.0.0'
+
+if (process.env.HOST) {
+  console.log(
+    chalk.cyan(
+      `Attempting to bind to HOST environment variable: ${chalk.yellow(
+        chalk.bold(process.env.HOST)
+      )}`
+    )
+  )
+  console.log(
+    'If this was unintentional, check that you haven\'t mistakenly set it in your shell.'
+  )
+  console.log(
+    `Learn more here: ${chalk.yellow('https://cra.link/advanced-config')}`
+  )
+  console.log()
+}
+
+// We require that you explicitly set browsers and do not fall back to
+// browserslist defaults.
+const { checkBrowsers } = require('react-dev-utils/browsersHelper')
+checkBrowsers(paths.appPath, isInteractive)
+  .then(() => {
+    // We attempt to use the default port but if it is busy, we offer the user to
+    // run on a different port. `choosePort()` Promise resolves to the next free port.
+    return choosePort(HOST, DEFAULT_PORT)
+  })
+  .then(port => {
+    if (port == null) {
+      // We have not found a port.
+      return
+    }
+
+    const config = configFactory('development')
+    const protocol = process.env.HTTPS === 'true' ? 'https' : 'http'
+    const appName = require(paths.appPackageJson).name
+
+    const useTypeScript = fs.existsSync(paths.appTsConfig)
+    const urls = prepareUrls(
+      protocol,
+      HOST,
+      port,
+      paths.publicUrlOrPath.slice(0, -1)
+    )
+    // Create a webpack compiler that is configured with custom messages.
+    const compiler = createCompiler({
+      appName,
+      config,
+      urls,
+      useYarn,
+      useTypeScript,
+      webpack
+    })
+    // Load proxy config
+    const proxySetting = require(paths.appPackageJson).proxy
+    const proxyConfig = prepareProxy(
+      proxySetting,
+      paths.appPublic,
+      paths.publicUrlOrPath
+    )
+    // Serve webpack assets generated by the compiler over a web server.
+    const serverConfig = {
+      ...createDevServerConfig(proxyConfig, urls.lanUrlForConfig),
+      host: HOST,
+      port
+    }
+    const devServer = new WebpackDevServer(serverConfig, compiler)
+    // Launch WebpackDevServer.
+    devServer.startCallback(() => {
+      if (isInteractive) {
+        clearConsole()
+      }
+
+      if (env.raw.FAST_REFRESH && semver.lt(react.version, '16.10.0')) {
+        console.log(
+          chalk.yellow(
+            `Fast Refresh requires React 16.10 or higher. You are using React ${react.version}.`
+          )
+        )
+      }
+
+      console.log(chalk.cyan('Starting the development server...\n'))
+      openBrowser(urls.localUrlForBrowser)
+    });
+
+    ['SIGINT', 'SIGTERM'].forEach(function (sig) {
+      process.on(sig, function () {
+        devServer.close()
+        process.exit()
+      })
+    })
+
+    if (process.env.CI !== 'true') {
+      // Gracefully exit when stdin ends
+      process.stdin.on('end', function () {
+        devServer.close()
+        process.exit()
+      })
+    }
+  })
+  .catch(err => {
+    if (err && err.message) {
+      console.log(err.message)
+    }
+    process.exit(1)
+  })
diff --git a/scripts/test.js b/scripts/test.js
new file mode 100644
index 0000000..ff3642f
--- /dev/null
+++ b/scripts/test.js
@@ -0,0 +1,51 @@
+'use strict'
+
+// Do this as the first thing so that any code reading it knows the right env.
+process.env.BABEL_ENV = 'test'
+process.env.NODE_ENV = 'test'
+process.env.PUBLIC_URL = ''
+
+// Makes the script crash on unhandled rejections instead of silently
+// ignoring them. In the future, promise rejections that are not handled will
+// terminate the Node.js process with a non-zero exit code.
+process.on('unhandledRejection', err => {
+  throw err
+})
+
+// Ensure environment variables are read.
+require('../config/env')
+
+const jest = require('jest')
+const execSync = require('child_process').execSync
+const argv = process.argv.slice(2)
+
+function isInGitRepository () {
+  try {
+    execSync('git rev-parse --is-inside-work-tree', { stdio: 'ignore' })
+    return true
+  } catch (e) {
+    return false
+  }
+}
+
+function isInMercurialRepository () {
+  try {
+    execSync('hg --cwd . root', { stdio: 'ignore' })
+    return true
+  } catch (e) {
+    return false
+  }
+}
+
+// Watch unless on CI or explicitly running all tests
+if (
+  !process.env.CI &&
+  argv.indexOf('--watchAll') === -1 &&
+  argv.indexOf('--watchAll=false') === -1
+) {
+  // https://github.com/facebook/create-react-app/issues/5210
+  const hasSourceControl = isInGitRepository() || isInMercurialRepository()
+  argv.push(hasSourceControl ? '--watch' : '--watchAll')
+}
+
+jest.run(argv)
diff --git a/src/components/QuantumLandscapeChart.js b/src/components/QuantumLandscapeChart.js
index 03ec146..20c6ccc 100644
--- a/src/components/QuantumLandscapeChart.js
+++ b/src/components/QuantumLandscapeChart.js
@@ -1,9 +1,11 @@
 // QuantumLandscapeChart.js
 
 import React from 'react'
+import SortingTable from './SortingTable.js'
 import * as d3 from 'd3'
 import '../viz-style.css'
-import csv from '../progress.csv'
+import progressCsv from '../progress.csv'
+import progressTable from '../progressTable.csv'
 
 const fontType = 'Helvetica'
 const smallLabelSize = 12 // font size in pixel
@@ -150,9 +152,9 @@ function scatterplot (
   // initiate svg
   svg = d3
     .select(chartTarget)
-    .on('mouseout touchend', (e) =>
-      redraw()
-    )
+    // .on('mouseout touchend', (e) =>
+    //  redraw()
+    // )
     .append('svg')
     .attr('viewBox', [0, 0, chartWidth, chartHeight])
     .attr('id', 'svgscatter')
@@ -739,9 +741,10 @@ function legend (circleSizeFields = 8) {
 };
 
 function QuantumLandscapeChart () {
+  const [tableJson, setTableJson] = React.useState([])
   React.useEffect(() => {
-  // Draw scatterplot from data
-    d3.csv(csv, (_d) => ({
+    // Draw scatterplot from data
+    d3.csv(progressCsv, (_d) => ({
       num_qubits: +_d.num_qubits,
       num_gates: +_d.num_gates,
       achieved: _d.achieved,
@@ -756,7 +759,17 @@ function QuantumLandscapeChart () {
       legend()
       window.onresize = redraw
     })
-  })
+    d3.csv(progressTable, (_d) => ({
+      metricName: _d.metricName,
+      metricValue: _d.metricValue,
+      evaluatedAt: _d.evaluatedAt,
+      submissionName: _d.submissionName,
+      taskName: _d.taskName,
+      submissionId: _d.submissionId
+    })).then((_d) => {
+      setTableJson(_d)
+    })
+  }, [])
 
   return (
     <span>
@@ -794,6 +807,44 @@ function QuantumLandscapeChart () {
           <p>If you have other data you would like to see added to this chart please either make a pull-request on Metriq to the <a href='https://github.com/unitaryfund/metriq-app/blob/main/src/progress.csv'>progress chart CSV file</a> directly or email <a href='mailto:metriq@unitary.fund'>metriq@unitary.fund</a>.</p>
         </div>
       </div>
+      <div className='row'>
+        <div className='col text-left'>
+          <SortingTable
+            className='detail-table'
+            columns={[{
+              title: 'Task',
+              key: 'taskName',
+              width: 250
+            },
+            {
+              title: 'Submission',
+              key: 'submissionName',
+              width: 250
+            },
+            {
+              title: 'Date',
+              key: 'evaluatedAt',
+              width: 250
+            },
+            {
+              title: 'Metric',
+              key: 'metricName',
+              width: 250
+            },
+            {
+              title: 'Value',
+              key: 'metricValue',
+              width: 250
+            }]}
+            data={tableJson}
+            onRowClick={(record) => { window.location.href = ('/Submission/' + record.submissionId) }}
+            tableLayout='auto'
+            rowClassName='link'
+            isCollapsible
+            collapseLabelNoun='results'
+          />
+        </div>
+      </div>
     </span>
   )
 }
diff --git a/src/components/QuantumVolumeChart.js b/src/components/QuantumVolumeChart.js
index d404c7a..50d79d2 100644
--- a/src/components/QuantumVolumeChart.js
+++ b/src/components/QuantumVolumeChart.js
@@ -5,7 +5,9 @@ import * as d3 from 'd3'
 import { saveAs } from 'file-saver'
 import XMLSerializer from 'xmlserializer'
 import '../viz-style.css'
-import csv from '../quantum_volume.csv'
+import axios from 'axios'
+import { sortByCounts } from './SortFunctions'
+import config from '../config'
 
 const fontType = 'Helvetica'
 const smallLabelSize = 15 // font size in pixel
@@ -23,7 +25,7 @@ const strokeOpacity = {
   achieved: 1,
   estimated: 0.7
 }
-const colors = ['#ED1010', '#0D99FF', '#56E1C8', '#FFB800']
+const colors = ['#ED1010', '#0D99FF', '#56E1C8', '#FFB800', '#c403f0', '#7c7c66', '#b82840', '#87b5f0', '#25fdf4']
 const domainIndex = {
   quantinuum: 0,
   ibmq: 1,
@@ -31,9 +33,8 @@ const domainIndex = {
   other: 3
 }
 const breakpoint = 700
-const parseDate = d3.utcParse('%m/%e/%Y')
 let isMobile = window.outerWidth < breakpoint
-let svg, d
+let svg, d, metricName, taskName, isQubits
 
 let areLabelsVisible = false
 function onLabelSwitchClick () {
@@ -50,6 +51,10 @@ function onScaleSwitchClick () {
   isScaleLinear = !isScaleLinear
   redraw()
 }
+function onMetricSelectChange (e) {
+  metricName = e.target.value
+  redraw()
+}
 function onDownloadClick () {
   const svgElement = d3.select('#my_dataviz').node()
   const svgString = new XMLSerializer().serializeToString(svgElement)
@@ -78,132 +83,107 @@ function hideLabels () {
   })
 }
 
-// Function to draw scatterplot
-function scatterplot (
+function barplot (
   data,
-  isScaleLinear = false,
-  xName = 'tableDate', // the x column
-  xAxisText = 'Date →',
-  yName = 'metricValue', // the y column
-  yAxisText = 'Quantum Volume  →',
-  chartTarget = '#my_dataviz', // html target element to attach chart
-  chartHeight = 600, // chart height
-  marginTop = 40, // top margin, in pixels
-  marginRight = 100, // right margin, in pixels
-  marginBottom = 70, // bottom margin, in pixels
-  xLabelShift = marginBottom - 40,
-  marginLeft = 100, // left margin, in pixels
-  rangeMult = 0.11,
-  xScaleType = d3.scaleTime,
-  horizontalLineStrokeSize = '1px',
-  tooltipLineStrokeTexture = '1 1',
-  tooltipLineColor = '#bbbbbb',
-  tooltipLineTextBorder = 2.5,
-  xlabelDistance = 19
+  xRange,
+  yRange,
+  Y,
+  marginLeft,
+  marginTop,
+  yAxisText
 ) {
-  data = data
-    .filter(
-      (d) =>
-        !isNaN(d[xName]) && d[xName] > 0 && !isNaN(d[yName]) && d[yName] > 0
-    )
-    .map(function (obj, index) {
-      return { ...obj, id: `ID_${index + 1}` }
-    })
-
-  // list of IDs of data with max values
-  // maxData with only max objects
-
-  let maxIDs = []
-  let currentMaxValue = -1
-
-  if (!isScaleLinear) {
-    data = data.slice(0, -3)
-  }
-
-  data.map((d) => {
-    if (!isScaleLinear) {
-      d.metricValue = Math.log2(d.metricValue)
-    }
-    if (Number(d.metricValue) > currentMaxValue) {
-      maxIDs = [...maxIDs, d.id]
-      currentMaxValue = Number(d.metricValue)
-    }
-
-    return 0
-  })
-
-  const yScaleType = isScaleLinear ? d3.scaleLinear : d3.scaleLog
-  if (!isScaleLinear) {
-    yAxisText = 'Algorithmic Qubits  →'
-  }
-
-  const maxData = data.filter((d) => maxIDs.includes(d.id))
-  maxData.map((d, i) => {
-    if (i < maxData.length - 1) {
-      d.nextX = maxData[i + 1][xName]
-      d.nextY = maxData[i + 1][yName]
-    }
-
-    return 0
-  })
-
-  // define aesthetic mappings
-  const x = (d) => d[xName]
-  const y = (d) => d[yName]
-
-  // width
-  const chartWidth = d3.select(chartTarget).node().getBoundingClientRect().width
-  if (isMobile) {
-    marginLeft = 80
-    marginRight = 50
-  }
-
-  // ranges
-  const xRange = [marginLeft, chartWidth - marginRight] // [left, right]
-  const yRange = [chartHeight - marginBottom, marginTop] // [bottom, top]
-
-  // values
-  const X = d3.map(data, x)
-  const Y = d3.map(data, y)
-  const I = d3.range(data.length)
+  const height = yRange[0] - yRange[1]
+  const yMin = d3.min(Y)
+  const yDomain = [yMin < 1 ? yMin : 1, d3.max(Y)]
+  console.log(yDomain)
+  console.log(yRange)
+  const x = d3.scaleBand()
+    .range([xRange[0], xRange[1]])
+    .domain(data.map((i) => { if (i.arXiv && areLabelsArxiv) { return `arXiv:${i.arXiv}` } else { return i.platformName ? i.platformName : i.methodName } }))
+    .padding(0.2)
+  svg.append('g')
+    .attr('transform', 'translate(0,' + height + ')')
+    .call(d3.axisBottom(x))
+    .selectAll('text')
+    .attr('transform', 'translate(-10,0)rotate(-45)')
+    .style('text-anchor', 'end')
 
-  // domains
-  const xDomain = [d3.min(X), d3.max(X)]
-  const yDomain = [1, d3.max(Y) + d3.max(Y) * rangeMult]
+  // Add Y axis
+  const y = (isScaleLinear ? d3.scaleLinear() : d3.scaleLog())
+    .range([0, yRange[0] - yRange[1]])
+    .domain([yDomain[1], yDomain[0]])
 
-  // scale
-  const xScale = xScaleType(xDomain, xRange)
-  const yScale = yScaleType(yDomain, yRange)
+  const yAxis = d3.axisLeft(y)
+  // append y axis
+  svg
+    .append('g')
+    .attr('transform', `translate(${marginLeft},0)`)
+    .attr('class', 'yaxis')
+    .style('font-size', `${smallLabelSize}px`)
+    .style('font-family', fontType)
+    .call(yAxis)
+    .call((g) =>
+      g
+        .append('text')
+        .attr('transform', 'rotate(270)')
+        .attr('x', -marginTop)
+        .attr('y', -50)
+        .attr('fill', 'currentColor')
+        .attr('text-anchor', 'end')
+        .attr('font-size', `${smallLabelSize}px`)
+        .text(yAxisText)
+    )
 
-  // time axes formatter
-  // For a less crowded x axis, especially if we increase fontsize for labels
-  const tickInterval = d3.timeMonth.every(12)
+  // Bars
+  svg.selectAll('bar')
+    .data(data)
+    .enter()
+    .append('rect')
+    .attr('x', (i) => { if (i.arXiv && areLabelsArxiv) { return x(`arXiv:${i.arXiv}`) } else { return x(i.platformName ? i.platformName : i.methodName) } })
+    .attr('y', (i) => y(i.metricValue))
+    .attr('width', x.bandwidth())
+    .attr('height', (i) => (height - y(i.metricValue)))
+    .style('stroke', (i) =>
+      colors[isQubits ? i.qubitCount - 1 : domainIndex[i.provider]]
+        ? colors[isQubits ? i.qubitCount - 1 : domainIndex[i.provider]]
+        : colors[3]
+    )
+    .style('fill', (i) =>
+      colors[isQubits ? i.qubitCount - 1 : domainIndex[i.provider]]
+        ? colors[isQubits ? i.qubitCount - 1 : domainIndex[i.provider]]
+        : colors[3]
+    )
+}
 
-  // axes
+function scatterplot (
+  data,
+  yName, // the y column
+  xName, // the x column
+  marginLeft,
+  marginRight, // right margin, in pixels
+  xlabelDistance,
+  I,
+  voronoi,
+  xRange,
+  xScale,
+  x,
+  yScale,
+  y,
+  maxIDs,
+  maxData,
+  tooltipLineColor,
+  tooltipLineStrokeTexture,
+  horizontalLineStrokeSize,
+  tickInterval,
+  marginTop,
+  marginBottom,
+  chartHeight,
+  chartWidth,
+  xLabelShift,
+  xAxisText,
+  yAxisText
+) {
   const xAxis = d3.axisBottom(xScale).ticks(tickInterval)
-  // Otherwise ticks label position in log scale must be declared manually
-  const yAxis = isScaleLinear ? d3.axisLeft(yScale).tickFormat(d3.format('~s')) : d3.axisLeft(yScale)
-
-  // voronoi generator
-  const dataForVoronoi = d3.map(I, (i) => [xScale(X[i]), yScale(Y[i])])
-  const voronoiRange = [xRange[0], yRange[1], xRange[1], yRange[0]]
-  const voronoi = d3.Delaunay.from(dataForVoronoi).voronoi(voronoiRange)
-
-  // generate tooltip
-  d3
-    .select('body')
-    .append('div')
-    .attr('id', 'scatter-tooltip')
-    .style('position', 'absolute')
-
-  // initiate svg
-  svg = d3
-    .select(chartTarget)
-    .append('svg')
-    .attr('viewBox', [0, 0, chartWidth, chartHeight])
-    .attr('id', 'svgscatter')
-    .attr('style', 'max-width: 100%')
-
   // append x axis
   svg
     .append('g')
@@ -222,7 +202,7 @@ function scatterplot (
         .attr('text-anchor', 'end')
         .text(xAxisText)
     )
-
+  const yAxis = isScaleLinear ? d3.axisLeft(yScale).tickFormat(d3.format('~s')) : d3.axisLeft(yScale)
   // append y axis
   svg
     .append('g')
@@ -269,66 +249,6 @@ function scatterplot (
     .style('stroke-width', horizontalLineStrokeSize)
     .style('stroke-dasharray', tooltipLineStrokeTexture)
 
-  // tooltip vlines
-  svg
-    .append('g')
-    .selectAll('line')
-    .data(data)
-    .join('line')
-    .attr('x1', (i) => xScale(x(i)))
-    .attr('y1', (i) => yScale(y(i)) + circleSize)
-    .attr('x2', (i) => xScale(x(i)))
-    .attr('y2', yScale(1))
-    .attr('id', (i) => i.id)
-    .style('visibility', 'hidden')
-    .style('stroke', tooltipLineColor)
-    .style('stroke-width', horizontalLineStrokeSize)
-    .style('stroke-dasharray', tooltipLineStrokeTexture)
-
-  // tooltip vline text
-  svg
-    .append('g')
-    .selectAll('text')
-    .data(data)
-    .join('text')
-    .attr('x', (i) => xScale(x(i)) + tooltipLineTextBorder)
-    .attr('y', yScale(1) - tooltipLineTextBorder)
-    .attr('id', (i) => i.id)
-    .style('visibility', 'hidden')
-    .style('font-size', `${smallLabelSize}px`)
-    .style('font-family', fontType)
-    .text((i) => d3.utcFormat('%B %Y')(x(i)))
-
-  // tooltip hlines
-  svg
-    .append('g')
-    .selectAll('line')
-    .data(data)
-    .join('line')
-    .attr('x1', (i) => xScale(x(i)) - circleSize)
-    .attr('y1', (i) => yScale(y(i)))
-    .attr('x2', xScale(d3.min(X)))
-    .attr('y2', (i) => yScale(y(i)))
-    .attr('id', (i) => i.id)
-    .style('visibility', 'hidden')
-    .style('stroke', tooltipLineColor)
-    .style('stroke-width', horizontalLineStrokeSize)
-    .style('stroke-dasharray', tooltipLineStrokeTexture)
-
-  // tooltip hline text
-  svg
-    .append('g')
-    .selectAll('text')
-    .data(data)
-    .join('text')
-    .attr('x', xScale(d3.min(X)) + tooltipLineTextBorder)
-    .attr('y', (i) => yScale(y(i)) - tooltipLineTextBorder)
-    .attr('id', (i) => i.id)
-    .style('visibility', 'hidden')
-    .style('font-size', `${smallLabelSize}px`)
-    .style('font-family', fontType)
-    .text((i) => d3.format('.2s')(y(i)))
-
   // voronoi grid
   svg
     .append('g')
@@ -372,13 +292,13 @@ function scatterplot (
     .attr('r', circleSize)
     .style('cursor', 'pointer')
     .style('stroke', (i) =>
-      colors[domainIndex[i.provider]]
-        ? colors[domainIndex[i.provider]]
+      colors[isQubits ? i.qubitCount - 1 : domainIndex[i.provider]]
+        ? colors[isQubits ? i.qubitCount - 1 : domainIndex[i.provider]]
         : colors[3]
     )
     .style('fill', (i) =>
-      colors[domainIndex[i.provider]]
-        ? colors[domainIndex[i.provider]]
+      colors[isQubits ? i.qubitCount - 1 : domainIndex[i.provider]]
+        ? colors[isQubits ? i.qubitCount - 1 : domainIndex[i.provider]]
         : colors[3]
     )
     .style('fill-opacity', (i) => circleOpacity.achieved)
@@ -389,7 +309,7 @@ function scatterplot (
     .attr('class', (i) => makeClass(x(i), y(i)))
     .attr('submissionId', (i) => i.submissionId)
     .attr('label', (i) => {
-      if (i.arXiv && areLabelsArxiv) { return `arXiv:${i.arXiv}` } else return i.platformName
+      if (i.arXiv && areLabelsArxiv) { return `arXiv:${i.arXiv}` } else return i.platformName ? i.platformName : i.methodName
     })
     .on('click', function () {
       if (!isMobile) {
@@ -452,6 +372,241 @@ function scatterplot (
   })
 }
 
+// Function to draw plot
+function plot (
+  data,
+  isScaleLinear = false,
+  yAxisText = 'Quantum Volume  →',
+  yName = 'metricValue', // the y column
+  xAxisText = 'Date →',
+  xName = 'tableDate', // the x column
+  chartTarget = '#my_dataviz', // html target element to attach chart
+  chartHeight = 600, // chart height
+  marginTop = 40, // top margin, in pixels
+  marginRight = 100, // right margin, in pixels
+  marginBottom = 70, // bottom margin, in pixels
+  xLabelShift = marginBottom - 40,
+  marginLeft = 100, // left margin, in pixels
+  rangeMult = 0.11,
+  xScaleType = d3.scaleTime,
+  horizontalLineStrokeSize = '1px',
+  tooltipLineStrokeTexture = '1 1',
+  tooltipLineColor = '#bbbbbb',
+  tooltipLineTextBorder = 2.5,
+  xlabelDistance = 19
+) {
+  data = data
+    .filter(
+      (x) => (!isNaN(x[xName]) && x[xName] > 0 && !isNaN(x[yName]) && x[yName] > 0)
+    )
+    .map(function (obj, index) {
+      return { ...obj, id: `ID_${index + 1}` }
+    })
+
+  // list of IDs of data with max values
+  // maxData with only max objects
+
+  let maxIDs = []
+  let currentMaxValue = -1
+
+  const isQv = (metricName.toLowerCase() === 'quantum volume')
+
+  data = data.filter((x) => x.metricName.toLowerCase() === metricName.toLowerCase())
+  if (isQv && !isScaleLinear) {
+    data = data.filter((x) => x.metricValue > 1)
+  }
+
+  data.map((d) => {
+    if (isQv && !isScaleLinear) {
+      d.metricValue = Math.log2(d.metricValue)
+    }
+    if (Number(d.metricValue) > currentMaxValue) {
+      maxIDs = [...maxIDs, d.id]
+      currentMaxValue = Number(d.metricValue)
+    }
+
+    return 0
+  })
+
+  const yScaleType = (isScaleLinear || isQv) ? d3.scaleLinear : d3.scaleLog
+  if (isQv && !isScaleLinear) {
+    yAxisText = 'Log-2 Quantum Volume  →'
+  }
+
+  const maxData = data.filter((d) => maxIDs.includes(d.id))
+  maxData.map((d, i) => {
+    if (i < maxData.length - 1) {
+      d.nextX = maxData[i + 1][xName]
+      d.nextY = maxData[i + 1][yName]
+    }
+
+    return 0
+  })
+
+  // define aesthetic mappings
+  const x = (d) => d[xName]
+  const y = (d) => d[yName]
+
+  // width
+  const chartWidth = d3.select(chartTarget).node().getBoundingClientRect().width
+  if (isMobile) {
+    marginLeft = 80
+    marginRight = 50
+  }
+
+  // ranges
+  const xRange = [marginLeft, chartWidth - marginRight] // [left, right]
+  const yRange = [chartHeight - marginBottom, marginTop] // [bottom, top]
+
+  // values
+  const X = d3.map(data, x)
+  const Y = d3.map(data, y)
+  const I = d3.range(data.length)
+
+  // domains
+  const yMin = d3.min(Y)
+  const yMax = d3.max(Y)
+  const xDomain = [d3.min(X), d3.max(X)]
+  const yDomain = [yMin < 1 ? yMin : 1, yMax > 1 ? (d3.max(Y) + d3.max(Y) * rangeMult) : 1]
+
+  // scale
+  const xScale = xScaleType(xDomain, xRange)
+  const yScale = yScaleType(yDomain, yRange)
+
+  // time axes formatter
+  // For a less crowded x axis, especially if we increase fontsize for labels
+  const tickInterval = d3.timeMonth.every(12)
+
+  // voronoi generator
+  const dataForVoronoi = d3.map(I, (i) => [xScale(X[i]), yScale(Y[i])])
+  const voronoiRange = [xRange[0], yRange[1], xRange[1], yRange[0]]
+  const voronoi = d3.Delaunay.from(dataForVoronoi).voronoi(voronoiRange)
+
+  // generate tooltip
+  d3
+    .select('body')
+    .append('div')
+    .attr('id', 'scatter-tooltip')
+    .style('position', 'absolute')
+
+  // initiate svg
+  svg = d3
+    .select(chartTarget)
+    .append('svg')
+    .attr('viewBox', [0, 0, chartWidth, chartHeight])
+    .attr('id', 'svgscatter')
+    .attr('style', 'max-width: 100%')
+
+  // tooltip vlines
+  svg
+    .append('g')
+    .selectAll('line')
+    .data(data)
+    .join('line')
+    .attr('x1', (i) => xScale(x(i)))
+    .attr('y1', (i) => yScale(y(i)) + circleSize)
+    .attr('x2', (i) => xScale(x(i)))
+    .attr('y2', yScale(1))
+    .attr('id', (i) => i.id)
+    .style('visibility', 'hidden')
+    .style('stroke', tooltipLineColor)
+    .style('stroke-width', horizontalLineStrokeSize)
+    .style('stroke-dasharray', tooltipLineStrokeTexture)
+
+  // tooltip vline text
+  svg
+    .append('g')
+    .selectAll('text')
+    .data(data)
+    .join('text')
+    .attr('x', (i) => xScale(x(i)) + tooltipLineTextBorder)
+    .attr('y', yScale(1) - tooltipLineTextBorder)
+    .attr('id', (i) => i.id)
+    .style('visibility', 'hidden')
+    .style('font-size', `${smallLabelSize}px`)
+    .style('font-family', fontType)
+    .text((i) => d3.utcFormat('%B %Y')(x(i)))
+
+  // tooltip hlines
+  svg
+    .append('g')
+    .selectAll('line')
+    .data(data)
+    .join('line')
+    .attr('x1', (i) => xScale(x(i)) - circleSize)
+    .attr('y1', (i) => yScale(y(i)))
+    .attr('x2', xScale(d3.min(X)))
+    .attr('y2', (i) => yScale(y(i)))
+    .attr('id', (i) => i.id)
+    .style('visibility', 'hidden')
+    .style('stroke', tooltipLineColor)
+    .style('stroke-width', horizontalLineStrokeSize)
+    .style('stroke-dasharray', tooltipLineStrokeTexture)
+
+  // tooltip hline text
+  svg
+    .append('g')
+    .selectAll('text')
+    .data(data)
+    .join('text')
+    .attr('x', xScale(d3.min(X)) + tooltipLineTextBorder)
+    .attr('y', (i) => yScale(y(i)) - tooltipLineTextBorder)
+    .attr('id', (i) => i.id)
+    .style('visibility', 'hidden')
+    .style('font-size', `${smallLabelSize}px`)
+    .style('font-family', fontType)
+    .text((i) => d3.format('.2s')(y(i)))
+
+  let isSameDate = true
+  for (let i = 1; i < data.length; ++i) {
+    if (data[0].tableDate !== data[i].tableDate) {
+      isSameDate = false
+      break
+    }
+  }
+
+  if (isSameDate) {
+    barplot(
+      data,
+      xRange,
+      [chartHeight - marginBottom / 2, marginTop],
+      Y,
+      marginLeft,
+      marginTop,
+      yAxisText
+    )
+  } else {
+    scatterplot(
+      data,
+      yName, // the y column
+      xName, // the x column
+      marginLeft,
+      marginRight, // right margin, in pixels
+      xlabelDistance,
+      I,
+      voronoi,
+      xRange,
+      xScale,
+      x,
+      yScale,
+      y,
+      maxIDs,
+      maxData,
+      tooltipLineColor,
+      tooltipLineStrokeTexture,
+      horizontalLineStrokeSize,
+      tickInterval,
+      marginTop,
+      marginBottom,
+      chartHeight,
+      chartWidth,
+      xLabelShift,
+      xAxisText,
+      yAxisText
+    )
+  }
+}
+
 function mousemove (
   e,
   marginRight,
@@ -542,7 +697,7 @@ function mousemove (
         <a href="https://metriq.info/Submission/${
           idData.submissionId
         }" style="color: ${
-          colors[domainIndex[idData.domain]]
+          colors[isQubits ? idData.qubitCount - 1 : domainIndex[idData.domain]]
         }; filter: brightness(0.85)">→ explore submission</a>
       </div>`
       )
@@ -597,7 +752,7 @@ function redraw () {
   d3.select('#svgscatter').remove()
   d3.select('#scatter-tooltip').remove()
   d3.selectAll('#svglegend').remove()
-  scatterplot(d, isScaleLinear)
+  plot(d, isScaleLinear, metricName)
   legend()
   window.scrollTo(0, scroll)
 }
@@ -625,6 +780,77 @@ function legend (circleSizeFields = 8) {
 
   let newY = circleSizeFields + 10
 
+  if (isQubits) {
+    // circle 1
+    svg
+      .append('circle')
+      .attr('stroke-width', strokeSize)
+      .attr('cx', circleSizeFields)
+      .attr('cy', newY)
+      .attr('r', circleSizeFields)
+      .style('stroke', colors[1])
+      .style('stroke-opacity', strokeOpacity.fieldLegend)
+      .style('fill', colors[1])
+      .style('fill-opacity', circleOpacity.fieldLegend)
+
+    // circle 1 label
+    svg
+      .append('text')
+      .attr('x', circleSizeFields * 2 + 15)
+      .attr('y', newY + 4)
+      .style('font-size', `${smallLabelSize}px`)
+      .style('font-family', fontType)
+      .text('2 qubits')
+
+    newY = newY + circleSizeFields + 20
+
+    // circle 2
+    svg
+      .append('circle')
+      .attr('stroke-width', strokeSize)
+      .attr('cx', circleSizeFields)
+      .attr('cy', newY)
+      .attr('r', circleSizeFields)
+      .style('stroke', colors[5])
+      .style('stroke-opacity', strokeOpacity.fieldLegend)
+      .style('fill', colors[5])
+      .style('fill-opacity', circleOpacity.fieldLegend)
+
+    // circle 2 label
+    svg
+      .append('text')
+      .attr('x', circleSizeFields * 2 + 15)
+      .attr('y', newY + 4)
+      .style('font-size', `${smallLabelSize}px`)
+      .style('font-family', fontType)
+      .text('6 qubits')
+
+    newY = newY + circleSizeFields + 20
+
+    // circle 3
+    svg
+      .append('circle')
+      .attr('stroke-width', strokeSize)
+      .attr('cx', circleSizeFields)
+      .attr('cy', newY)
+      .attr('r', circleSizeFields)
+      .style('stroke', colors[6])
+      .style('stroke-opacity', strokeOpacity.fieldLegend)
+      .style('fill', colors[6])
+      .style('fill-opacity', circleOpacity.fieldLegend)
+
+    // circle 3 label
+    svg
+      .append('text')
+      .attr('x', circleSizeFields * 2 + 15)
+      .attr('y', newY + 4)
+      .style('font-size', `${smallLabelSize}px`)
+      .style('font-family', fontType)
+      .text('7 qubits')
+
+    return
+  }
+
   // circle 1
   svg
     .append('circle')
@@ -720,64 +946,144 @@ function makeClass (x, y) {
   return `c${x - y}`
 }
 
-function QuantumVolumeChart () {
+function QuantumVolumeChart (props) {
+  const [metricNames, setMetricNames] = React.useState([])
+  const [taskId, setTaskId] = React.useState(0)
+  const [isQ, setIsQ] = React.useState(false)
   React.useEffect(() => {
-  // Draw scatterplot from data
-    d3.csv(csv, (d) => ({
-      key: +d.key,
-      name: d.name,
-      submissionId: d.submissionId,
-      platformName: d.platformName,
-      methodName: d.methodName,
-      metricName: d.metricName,
-      metricValue: +d.metricValue,
-      qubitCount: +d.qubitCount,
-      circuitDepth: +d.circuitDepth,
-      provider: d.provider,
-      tableDate: parseDate(d.tableDate),
-      arXiv: d.arXiv
-    })).then((_d) => {
-      d = _d
-      redraw()
-      window.onresize = redraw
-    })
-  })
+    isScaleLinear = (parseInt(props.taskId) !== 34)
+    isQubits = !!(props.isQubits)
+    if (isQubits !== isQ) {
+      setIsQ(isQubits)
+    }
+    if (taskId === props.taskId) {
+      return
+    }
+    setTaskId(props.taskId)
+    // Draw scatterplot from data
+    const taskRoute = config.api.getUriPrefix() + '/task/' + props.taskId
+    axios.get(taskRoute)
+      .then(res => {
+        const task = res.data.data
+        taskName = task.fullName
+        task.childTasks.sort(sortByCounts)
+        if (props.onLoadData) {
+          props.onLoadData(task)
+        }
+        const results = task.results
+        metricName = ''
+        const metricNameCounts = []
+        for (let i = 0; i < results.length; ++i) {
+          if (results[i].submissionUrl.toLowerCase().startsWith('https://arxiv.org/')) {
+            const parts = results[i].submissionUrl.split('/')
+            results[i].arXiv = (parts[parts.length - 1] === '') ? parts[parts.length - 2] : parts[parts.length - 1]
+          } else {
+            results[i].arXiv = results[i].methodName
+          }
+
+          if (metricNames.includes(results[i].metricName)) {
+            ++metricNameCounts[metricNames.indexOf(results[i].metricName)]
+          } else {
+            metricNames.push(results[i].metricName)
+            metricNameCounts.push(1)
+            if (results[i].metricName.toLowerCase() === 'quantum volume') {
+              metricName = 'quantum volume'
+            } else if (isQubits && (results[i].metricName.toLowerCase() === 'iterations to 1e-3 error')) {
+              metricName = 'iterations to 1e-3 error'
+            }
+          }
+        }
+        metricNames.sort((a, b) => a > b)
+        if (metricName === '') {
+          const maxCount = metricNameCounts[0]
+          let maxCountIndex = 0
+          for (let i = 1; i < metricNames.length; ++i) {
+            if (metricNameCounts[i] > maxCount) {
+              maxCountIndex = i
+            }
+          }
+          metricName = metricNames[maxCountIndex]
+          if (maxCountIndex > 0) {
+            const tmp = metricNames[0]
+            metricNames[0] = metricNames[maxCountIndex]
+            metricNames[maxCountIndex] = tmp
+          }
+        } else {
+          const tmp = metricNames.indexOf(metricName)
+          if (tmp > 0) {
+            metricNames[tmp] = metricNames[0]
+            metricNames[0] = metricName
+          }
+        }
+        setMetricNames(metricNames)
+        d = results
+          .map((_d) => ({
+            key: +_d.id,
+            submissionId: +_d.submissionId,
+            platformName: _d.platformName,
+            methodName: _d.methodName,
+            metricName: _d.metricName,
+            metricValue: _d.metricValue,
+            qubitCount: _d.qubitCount ? +_d.qubitCount : '',
+            circuitDepth: _d.circuitDepth ? +_d.circuitDepth : '',
+            provider: _d.providerName ? _d.providerName.toLowerCase() : 'Other',
+            tableDate: Date.parse(_d.evaluatedAt),
+            arXiv: _d.arXiv
+          }))
+          .sort((a, b) => isQubits ? (a.qubitCount < b.qubitCount) : (a.tableDate > b.tableDate))
+        redraw()
+      })
+      .catch(err => {
+        window.alert('Could not load task! Check your connection and reload the page. (Error: ' + err + ')')
+      })
+  }, [props, metricNames, taskId, setTaskId, isQ, setIsQ])
 
   return (
     <span>
       <div className='row'>
         <div className='col text-left'>
-          <h4 align='left'>Quantum Volume</h4>
+          <h4 align='left'>{taskName}</h4>
         </div>
       </div>
       <div id='cargo'>
         <div id='my_dataviz' />
         <div id='legend_guide'>
           <div>
+            {!isQ &&
+              <span>
+                <div id='legend-switch' style={{ marginTop: '10px' }}>
+                  <label className='switch'>
+                    <input id='labelSwitch' type='checkbox' onClick={onLabelSwitchClick} />
+                    <span className='slider round' />
+                  </label>
+                  <span className='legendTitle'>Show labels</span>
+                </div>
+                <div id='legend-switch' style={{ marginTop: '10px' }}>
+                  <label className='switch'>
+                    <input id='arXivSwitch' type='checkbox' onClick={onArxivSwitchClick} />
+                    <span className='slider round' />
+                  </label>
+                  <span className='legendTitle'>Labels | ID</span>
+                </div>
+              </span>}
             <div id='legend-switch' style={{ marginTop: '10px' }}>
               <label className='switch'>
-                <input id='labelSwitch' type='checkbox' onClick={onLabelSwitchClick} />
-                <span className='slider round' />
-              </label>
-              <span className='legendTitle'>Show labels</span>
-            </div>
-            <div id='legend-switch' style={{ marginTop: '10px' }}>
-              <label className='switch'>
-                <input id='arXivSwitch' type='checkbox' onClick={onArxivSwitchClick} />
+                <input id='isScaleLinearSwitch' type='checkbox' onClick={onScaleSwitchClick} />
                 <span className='slider round' />
               </label>
-              <span className='legendTitle'>Labels | ID</span>
+              <span className='legendTitle'>{isScaleLinear ? 'Linear | Log' : 'Log | Linear'}</span>
             </div>
             <div id='legend-switch' style={{ marginTop: '10px' }}>
-              <label className='switch'>
-                <input id='isScaleLinearSwitch' type='checkbox' onClick={onScaleSwitchClick} />
-                <span className='slider round' />
+              <label className='switch' style={{ width: '50%' }}>
+                <select id='metricSelect' style={{ width: '100%' }} onChange={onMetricSelectChange}>
+                  {metricNames.map((option, index) => <option key={index} value={option}>{option}</option>)}
+                </select>
               </label>
-              <span className='legendTitle'>Log | Linear</span>
+              <span className='legendTitle' style={{ width: '50%', marginTop: '10px' }}> Metric</span>
             </div>
           </div>
           <div>
-            <span className='legendTitle'>Providers</span>
+            <span className='legendTitle'>{isQ ? 'Qubits' : 'Providers'}</span>
             <div id='legend-color' style={{ marginTop: '10px' }} />
           </div>
           <div>
diff --git a/src/components/SotaChart.js b/src/components/SotaChart.js
index cd4471c..033f3f6 100644
--- a/src/components/SotaChart.js
+++ b/src/components/SotaChart.js
@@ -3,9 +3,8 @@
 // and https://betterprogramming.pub/react-d3-plotting-a-line-chart-with-tooltips-ed41a4c31f4f
 
 import React from 'react'
-import { Chart, LinearScale, LogarithmicScale, TimeScale, PointElement, LineElement, ScatterController, Tooltip } from 'chart.js'
+import { Chart, LinearScale, LogarithmicScale, TimeScale, CategoryScale, PointElement, LineElement, BarElement, ScatterController, BarController, Tooltip } from 'chart.js'
 import ChartDataLabels from 'chartjs-plugin-datalabels'
-import { BarWithErrorBarsChart } from 'chartjs-chart-error-bars'
 import moment from 'moment'
 import 'chartjs-adapter-moment'
 import axios from 'axios'
@@ -31,10 +30,10 @@ const percentileZ = (p) => {
            ((((3.1308291 * r - 21.0622410) * r + 23.0833674) * r - 8.4735109) * r + 1)
 }
 
-const chartComponents = [LinearScale, LogarithmicScale, TimeScale, PointElement, LineElement, ScatterController, Tooltip, ChartDataLabels]
+const chartComponents = [LinearScale, LogarithmicScale, TimeScale, CategoryScale, PointElement, LineElement, BarElement, ScatterController, BarController, Tooltip, ChartDataLabels]
 Chart.register(chartComponents)
 Chart.defaults.font.size = 13
-let chart = null
+const chart = null
 
 class SotaChart extends React.Component {
   constructor (props) {
@@ -316,8 +315,7 @@ class SotaChart extends React.Component {
         datasets: [],
         labels: d.map((obj, index) => ((state.label === 'arXiv') && obj.arXivId) ? (obj.arXivId + '\n') : (obj.method + (obj.platform ? '\n' + obj.platform : '')))
       }
-    }
-    if (!isSameDate) {
+    } else {
       const dataSotaLine = state.isSotaLineVisible
         ? {
             type: 'line',
@@ -455,10 +453,10 @@ class SotaChart extends React.Component {
         }
         ++color
         color = color % 6
+        for (let x = 0; x < subsets[key].length; ++x) {
+          subsets[key][x].rgb = rgb
+        }
         if (state.subsetDataSetsActive.get(key) ?? true) {
-          for (let x = 0; x < subsets[key].length; ++x) {
-            subsets[key][x].rgb = rgb
-          }
           fullSet = fullSet.concat(subsets[key])
         }
       }
@@ -696,7 +694,7 @@ class SotaChart extends React.Component {
       if (this.state.chart) {
         this.state.chart.destroy()
       }
-      chart = new BarWithErrorBarsChart(document.getElementById('sota-chart-canvas-' + this.props.chartId).getContext('2d'), { data, options })
+      const chart = new Chart(document.getElementById('sota-chart-canvas-' + this.props.chartId).getContext('2d'), { type: isSameDate ? 'bar' : undefined, data, options })
       this.setState({ chart, subsetDataSets })
     }
     chartFunc()
diff --git a/src/progress.csv b/src/progress.csv
index 6b6838e..bb254ca 100644
--- a/src/progress.csv
+++ b/src/progress.csv
@@ -3,8 +3,8 @@ Quantum supremacy using a programmable superconducting processor,"Nature volume
 Strong quantum computational advantage using a superconducting quantum processor,"Phys. Rev. Lett. 127, 180501 (2021-10-25)",2021,Quantum supremacy,Random circuit sampling,47,69,56,1590,true
 Evidence for the utility of quantum computing before fault tolerance,"Nature volume 618, pages 500–505 (2023)",2023,Quantum supremacy,2D transverse-field Ising model,195,653,127,14400,true
 "Quantinuum H-Series quantum computer accelerates through 3 more performance records for quantum volume: 2^17, 2^18, and 2^19",Quantinuum blog post (2023),2023,Quantum supremacy,Quantum volume,34,642,19,532,true
-How to compute a 256-bit elliptic curve private key with only 50 million Toffoli gates,arXiv:2306.08585,2023,Cryptography,Factoring,4,696,1152,50000000,false
-A Threshold for Quantum Advantage in Derivative Pricing,arXiv:2012.03819,2020,Finance,Derivative pricing,0,697,8000,54000000,false
+How to compute a 256-bit elliptic curve private key with only 50 million Toffoli gates,arXiv:2306.08585,2023,Cryptography,ECDLP,4,696,1152,50000000,false
+A Threshold for Quantum Advantage in Derivative Pricing,arXiv:2012.03819,2020,Finance,Derivative pricing,0,697,8000,12000000000,false
 Towards Quantum Advantage in Financial Market Risk using Quantum Gradient Algorithms,arXiv:2111.12509,2021,Finance,Derivative pricing,0,698,12000,12000000000,false
 Using Q# to estimate resources needed for quantum advantage in derivative pricing,https://cloudblogs.microsoft.com/quantum/2022/09/15/using-q-to-estimate-resources-needed-for-quantum-advantage-in-derivative-pricing/,2022,Finance,Derivative pricing,0,699,,,false
 Derivative Pricing using Quantum Signal Processing,arXiv:2307.14310,2023,Finance,Derivative pricing,0,700,4700,1000000000,false
diff --git a/src/progressTable.csv b/src/progressTable.csv
new file mode 100644
index 0000000..8eccbe6
--- /dev/null
+++ b/src/progressTable.csv
@@ -0,0 +1,181 @@
+id,isHigherBetter,metricName,metricValue,createdAt,updatedAt,deletedAt,userId,submissionMethodRefId,submissionTaskRefId,evaluatedAt,notes,standardError,sampleSize,submissionPlatformRefId,qubitCount,circuitDepth,shots,submissionDataSetRefId,id,createdAt,updatedAt,deletedAt,userId,submissionId,taskId,id,name,nameNormal,description,contentUrl,thumbnailUrl,approvedAt,createdAt,updatedAt,userId,deletedAt,publishedAt,codeUrl,supplementUrl,id,name,fullName,description,createdAt,updatedAt,userId,taskId,isHideChart,submissionName,submissionId,taskName
+1750,f,Toffoli count,1.733085723671708e+157,2024-03-05 15:15:57.37+00,2024-03-05 15:15:57.37+00,,19,1042,1081,2022-09-27,"k=32, n=1048576",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1749,f,Toffoli count,4.033246320765524e+147,2024-03-05 15:15:45.285+00,2024-03-05 15:15:45.285+00,,19,1042,1081,2022-09-27,"k=32, n=524288",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1748,f,Toffoli count,9.381753075466702e+137,2024-03-05 15:15:33.673+00,2024-03-05 15:15:33.673+00,,19,1042,1081,2022-09-27,"k=32, n=262144",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1747,f,Toffoli count,2.180230014313224e+128,2024-03-05 15:15:21.442+00,2024-03-05 15:15:21.442+00,,19,1042,1081,2022-09-27,"k=32, n=131072",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1746,f,Toffoli count,5.057065857402561e+118,2024-03-05 15:15:08.974+00,2024-03-05 15:15:08.974+00,,19,1042,1081,2022-09-27,"k=32, n=65536",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1745,f,Toffoli count,2.679848788824798e+99,2024-03-05 15:14:56.967+00,2024-03-05 15:14:56.967+00,,19,1042,1081,2022-09-27,"k=32, n=16384",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1744,f,Toffoli count,1.1685579939783203e+109,2024-03-05 15:14:42.353+00,2024-03-05 15:14:42.353+00,,19,1042,1081,2022-09-27,"k=32, n=32768",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1743,f,Toffoli count,6.053095236734529e+89,2024-03-05 15:14:28.85+00,2024-03-05 15:14:28.85+00,,19,1042,1081,2022-09-27,"k=32, n=8192",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1742,f,Toffoli count,1.32623628475831e+80,2024-03-05 15:14:14.571+00,2024-03-05 15:14:14.571+00,,19,1042,1081,2022-09-27,"k=32, n=4096",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1741,f,Toffoli count,1.32623628475831e+80,2024-03-05 15:14:00.972+00,2024-03-05 15:14:00.972+00,,19,1042,1081,2022-09-27,"k=32, n=4096",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1740,f,Toffoli count,2.733144483411872e+70,2024-03-05 15:13:45.065+00,2024-03-05 15:13:45.065+00,,19,1042,1081,2022-09-27,"k=32, n=2048",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1739,f,Toffoli count,4.975934516475092e+60,2024-03-05 15:13:34.457+00,2024-03-05 15:13:34.457+00,,19,1042,1081,2022-09-27,"k=32, n=1024",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1738,f,Toffoli count,7.028142536559382e+50,2024-03-05 15:13:24.507+00,2024-03-05 15:13:24.507+00,,19,1042,1081,2022-09-27,"k=32, n=512",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1737,f,Toffoli count,5.824459402923075e+40,2024-03-05 15:13:15.954+00,2024-03-05 15:13:15.954+00,,19,1042,1081,2022-09-27,"k=32, n=256",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1736,f,Toffoli count,1.4778069215022808e+30,2024-03-05 15:13:07.371+00,2024-03-05 15:13:07.371+00,,19,1042,1081,2022-09-27,"k=32, n=128",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1735,f,Toffoli count,1.8326241409425905e+18,2024-03-05 15:12:58.188+00,2024-03-05 15:12:58.188+00,,19,1042,1081,2022-09-27,"k=32, n=64",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1734,f,Toffoli count,1.5573982593804497e+78,2024-03-05 15:12:40.716+00,2024-03-05 15:12:40.716+00,,19,1042,1081,2022-09-27,"k=16, n=524288",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1733,f,Toffoli count,2.3758572837246226e+73,2024-03-05 15:12:28.349+00,2024-03-05 15:12:28.349+00,,19,1042,1081,2022-09-27,"k=16, n=262144",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1732,f,Toffoli count,3.623611278043943e+68,2024-03-05 15:12:14.182+00,2024-03-05 15:12:14.182+00,,19,1042,1081,2022-09-27,"k=16, n=131072",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1731,f,Toffoli count,5.524131605530658e+63,2024-03-05 15:11:57.655+00,2024-03-05 15:11:57.655+00,,19,1042,1081,2022-09-27,"k=16, n=65536",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1730,f,Toffoli count,5.524131605530658e+63,2024-03-05 15:09:12.411+00,2024-03-05 15:09:12.411+00,,19,1042,1081,2022-09-27,"k=16, n=65536",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1729,f,Toffoli count,8.413732123974806e+58,2024-03-05 15:08:58.692+00,2024-03-05 15:08:58.692+00,,19,1042,1081,2022-09-27,"k=16, n=32768",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1728,f,Toffoli count,1.2791384791344545e+54,2024-03-05 15:08:46.507+00,2024-03-05 15:08:46.507+00,,19,1042,1081,2022-09-27,"k=16, n=16384",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1727,f,Toffoli count,1.937553720786852e+49,2024-03-05 15:08:32.865+00,2024-03-05 15:08:32.865+00,,19,1042,1081,2022-09-27,"k=16, n=8192",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1726,f,Toffoli count,2.9133993758634907e+44,2024-03-05 15:06:16.845+00,2024-03-05 15:06:16.845+00,,19,1042,1081,2022-09-27,"k=16, n=4096",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1725,f,Toffoli count,4.3166641429934056e+39,2024-03-05 15:06:07.515+00,2024-03-05 15:06:07.515+00,,19,1042,1081,2022-09-27,"k=16, n=2048",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1724,f,Toffoli count,6.209077488489386e+34,2024-03-05 15:05:55.502+00,2024-03-05 15:05:55.502+00,,19,1042,1081,2022-09-27,"k=16, n=1024",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1723,f,Toffoli count,8.41141456821158e+29,2024-03-05 15:05:46.172+00,2024-03-05 15:05:46.172+00,,19,1042,1081,2022-09-27,"k=16, n=512",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1722,f,Toffoli count,1.0078751602022314e+25,2024-03-05 15:05:34.397+00,2024-03-05 15:05:34.397+00,,19,1042,1081,2022-09-27,"k=16, n=256",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1721,f,Toffoli count,9.334302120126218e+19,2024-03-05 15:05:24.205+00,2024-03-05 15:05:24.205+00,,19,1042,1081,2022-09-27,"k=16, n=128",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1720,f,Toffoli count,488526937079580,2024-03-05 15:05:12.764+00,2024-03-05 15:05:12.764+00,,19,1042,1081,2022-09-27,"k=16, n=64",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1719,f,Toffoli count,601080390,2024-03-05 15:05:03.082+00,2024-03-05 15:05:03.082+00,,19,1042,1081,2022-09-27,"k=16, n=32",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1718,f,Toffoli count,5.5303480472357946e+38,2024-03-05 15:04:26.539+00,2024-03-05 15:04:26.539+00,,19,1042,1081,2022-09-27,"k=8, n=262144",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1717,f,Toffoli count,2.1600614675713643e+36,2024-03-05 15:04:10.309+00,2024-03-05 15:04:10.309+00,,19,1042,1081,2022-09-27,"k=8, n=131072",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1716,f,Toffoli count,8.435937701339247e+33,2024-03-05 15:03:58.004+00,2024-03-05 15:03:58.004+00,,19,1042,1081,2022-09-27,"k=8, n=65536",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1715,f,Toffoli count,3.2938804052325523e+31,2024-03-05 15:03:46.186+00,2024-03-05 15:03:46.186+00,,19,1042,1081,2022-09-27,"k=8, n=32768",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1714,f,Toffoli count,1.2855728001997967e+29,2024-03-05 15:03:33.238+00,2024-03-05 15:03:33.238+00,,19,1042,1081,2022-09-27,"k=8, n=16384",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1713,f,Toffoli count,5.013190031671481e+26,2024-03-05 15:03:23.252+00,2024-03-05 15:03:23.252+00,,19,1042,1081,2022-09-27,"k=8, n=8192",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1712,f,Toffoli count,1.9515893375809206e+24,2024-03-05 15:03:12.944+00,2024-03-05 15:03:12.944+00,,19,1042,1081,2022-09-27,"k=8, n=4096",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1711,f,Toffoli count,7.571365534761592e+21,2024-03-05 15:02:57.883+00,2024-03-05 15:02:57.883+00,,19,1042,1081,2022-09-27,"k=8, n=2048",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1710,f,Toffoli count,2.9172576776381825e+19,2024-03-05 15:02:47.406+00,2024-03-05 15:02:47.406+00,,19,1042,1081,2022-09-27,"k=8, n=1024",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1709,f,Toffoli count,1.1085923125474912e+17,2024-03-05 15:02:35.93+00,2024-03-05 15:02:35.93+00,,19,1042,1081,2022-09-27,"k=8, n=512",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1708,f,Toffoli count,409663695276000,2024-03-05 15:02:25.26+00,2024-03-05 15:02:25.26+00,,19,1042,1081,2022-09-27,"k=8, n=256",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1707,f,Toffoli count,1429702652400,2024-03-05 15:02:14.488+00,2024-03-05 15:02:14.488+00,,19,1042,1081,2022-09-27,"k=8, n=128",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1706,f,Toffoli count,4426165368,2024-03-05 15:02:03.655+00,2024-03-05 15:02:03.655+00,,19,1042,1081,2022-09-27,"k=8, n=64",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1705,f,Toffoli count,10518300,2024-03-05 15:01:50.01+00,2024-03-05 15:01:50.01+00,,19,1042,1081,2022-09-27,"k=8, n=32",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1704,f,Toffoli count,12870,2024-03-05 15:01:23.632+00,2024-03-05 15:01:23.632+00,,19,1042,1081,2022-09-27,"k=8, n=16",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1703,f,Toffoli count,1.2297266440393687e+19,2024-03-05 13:16:20.121+00,2024-03-05 13:16:20.121+00,,19,1042,1081,2022-09-27,"k=4, n=131072",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1702,f,Toffoli count,7.685439696288973e+17,2024-03-05 13:16:06.57+00,2024-03-05 13:16:06.57+00,,19,1042,1081,2022-09-27,"k=4, n=65536",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1701,f,Toffoli count,4.802960042438656e+16,2024-03-05 13:15:50.59+00,2024-03-05 13:15:50.59+00,,19,1042,1081,2022-09-27,"k=4, n=32768",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1700,f,Toffoli count,3.001300362981376e+15,2024-03-05 13:15:39.388+00,2024-03-05 13:15:39.388+00,,19,1042,1081,2022-09-27,"k=4, n=16384",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1699,f,Toffoli count,187512576276480,2024-03-05 13:15:26.492+00,2024-03-05 13:15:26.492+00,,19,1042,1081,2022-09-27,"k=4, n=8192",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1698,f,Toffoli count,11710951848960,2024-03-05 13:15:12.433+00,2024-03-05 13:15:12.433+00,,19,1042,1081,2022-09-27,"k=4, n=4096",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1697,f,Toffoli count,730862190080,2024-03-05 13:14:54.957+00,2024-03-05 13:14:54.957+00,,19,1042,1081,2022-09-27,"k=4, n=2048",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1696,f,Toffoli count,45545029376,2024-03-05 13:14:39.311+00,2024-03-05 13:14:39.311+00,,19,1042,1081,2022-09-27,"k=4, n=1024",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1695,f,Toffoli count,2829877120,2024-03-05 13:13:44.577+00,2024-03-05 13:13:44.577+00,,19,1042,1081,2022-09-27,"k=4, n=512",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1694,f,Toffoli count,174792640,2024-03-05 13:13:31.042+00,2024-03-05 13:13:31.042+00,,19,1042,1081,2022-09-27,"k=4, n=256",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1693,f,Toffoli count,10668000,2024-03-05 13:12:55.483+00,2024-03-05 13:12:55.483+00,,19,1042,1081,2022-09-27,"k=4, n=128",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1692,f,Toffoli count,635376,2024-03-05 13:12:19.884+00,2024-03-05 13:12:19.884+00,,19,1042,1081,2022-09-27,"k=4, n=64",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1691,f,Toffoli count,35960,2024-03-05 13:11:54.798+00,2024-03-05 13:11:54.798+00,,19,1042,1081,2022-09-27,"k=4, n=32",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1690,f,Toffoli count,1820,2024-03-05 13:11:38.28+00,2024-03-05 13:11:38.28+00,,19,1042,1081,2022-09-27,"k=4, n=16",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1689,f,Toffoli count,70,2024-03-05 13:11:19.081+00,2024-03-05 13:11:19.081+00,,19,1042,1081,2022-09-27,"k=4, n=8",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1686,f,T-count,99000000000,2024-02-07 02:53:01.518+00,2024-02-07 02:53:01.518+00,,19,1053,1085,2019-05-23,"",,,,9237,,,,1085,2024-02-07 02:51:18.552+00,2024-02-07 02:51:18.552+00,,19,761,211,761,How to factor 2048 bit RSA integers in 8 hours using 20 million noisy qubits,how to factor 2048 bit rsa integers in 8 hours using 20 million noisy qubits,"We significantly reduce the cost of factoring integers and computing discrete logarithms in finite fields on a quantum computer by combining techniques from Shor 1994, Griffiths-Niu 1996, Zalka 2006, Fowler 2012, Ekerå-Håstad 2017, Ekerå 2017, Ekerå 2018, Gidney-Fowler 2019, Gidney 2019. We estimate the approximate cost of our construction using plausible physical assumptions for large-scale superconducting qubit platforms: a planar grid of qubits with nearest-neighbor connectivity, a characteristic physical gate error rate of $10^{-3}$, a surface code cycle time of 1 microsecond, and a reaction time of 10 microseconds. We account for factors that are normally ignored such as noise, the need to make repeated attempts, and the spacetime layout of the computation. When factoring 2048 bit RSA integers, our construction's spacetime volume is a hundredfold less than comparable estimates from earlier works (Van Meter et al. 2009, Jones et al. 2010, Fowler et al. 2012, Gheorghiu et al. 2019). In the abstract circuit model (which ignores overheads from distillation, routing, and error correction) our construction uses $3 n + 0.002 n \lg n$ logical qubits, $0.3 n^3 + 0.0005 n^3 \lg n$ Toffolis, and $500 n^2 + n^2 \lg n$ measurement depth to factor $n$-bit RSA integers. We quantify the cryptographic implications of our work, both for RSA and for schemes based on the DLP in finite fields.",https://arxiv.org/abs/1905.09749,"",,2024-02-07 02:50:27.442+00,2024-02-07 02:50:27.442+00,19,,2024-02-07 02:50:27.442+00,"","",211,Cryptography,Cryptography (Factoring),These are resource estimates for the scale of quantum computers necessary to do useful work in factoring encryption keys.,2024-02-07 02:40:18.386+00,2024-02-29 12:21:10.151+00,19,59,f,How to factor 2048 bit RSA integers in 8 hours using 20 million noisy qubits,761,Cryptography
+1685,f,T-count,2700000000,2024-02-07 02:52:09.284+00,2024-02-07 02:52:09.284+00,,19,1052,1085,2019-05-23,"",,,,6158,,,,1085,2024-02-07 02:51:18.552+00,2024-02-07 02:51:18.552+00,,19,761,211,761,How to factor 2048 bit RSA integers in 8 hours using 20 million noisy qubits,how to factor 2048 bit rsa integers in 8 hours using 20 million noisy qubits,"We significantly reduce the cost of factoring integers and computing discrete logarithms in finite fields on a quantum computer by combining techniques from Shor 1994, Griffiths-Niu 1996, Zalka 2006, Fowler 2012, Ekerå-Håstad 2017, Ekerå 2017, Ekerå 2018, Gidney-Fowler 2019, Gidney 2019. We estimate the approximate cost of our construction using plausible physical assumptions for large-scale superconducting qubit platforms: a planar grid of qubits with nearest-neighbor connectivity, a characteristic physical gate error rate of $10^{-3}$, a surface code cycle time of 1 microsecond, and a reaction time of 10 microseconds. We account for factors that are normally ignored such as noise, the need to make repeated attempts, and the spacetime layout of the computation. When factoring 2048 bit RSA integers, our construction's spacetime volume is a hundredfold less than comparable estimates from earlier works (Van Meter et al. 2009, Jones et al. 2010, Fowler et al. 2012, Gheorghiu et al. 2019). In the abstract circuit model (which ignores overheads from distillation, routing, and error correction) our construction uses $3 n + 0.002 n \lg n$ logical qubits, $0.3 n^3 + 0.0005 n^3 \lg n$ Toffolis, and $500 n^2 + n^2 \lg n$ measurement depth to factor $n$-bit RSA integers. We quantify the cryptographic implications of our work, both for RSA and for schemes based on the DLP in finite fields.",https://arxiv.org/abs/1905.09749,"",,2024-02-07 02:50:27.442+00,2024-02-07 02:50:27.442+00,19,,2024-02-07 02:50:27.442+00,"","",211,Cryptography,Cryptography (Factoring),These are resource estimates for the scale of quantum computers necessary to do useful work in factoring encryption keys.,2024-02-07 02:40:18.386+00,2024-02-29 12:21:10.151+00,19,59,f,How to factor 2048 bit RSA integers in 8 hours using 20 million noisy qubits,761,Cryptography
+1683,f,T-count,400000000,2024-02-07 02:51:43.374+00,2024-02-07 02:51:43.374+00,,19,1051,1085,2019-05-23,"",,,,3078,,,,1085,2024-02-07 02:51:18.552+00,2024-02-07 02:51:18.552+00,,19,761,211,761,How to factor 2048 bit RSA integers in 8 hours using 20 million noisy qubits,how to factor 2048 bit rsa integers in 8 hours using 20 million noisy qubits,"We significantly reduce the cost of factoring integers and computing discrete logarithms in finite fields on a quantum computer by combining techniques from Shor 1994, Griffiths-Niu 1996, Zalka 2006, Fowler 2012, Ekerå-Håstad 2017, Ekerå 2017, Ekerå 2018, Gidney-Fowler 2019, Gidney 2019. We estimate the approximate cost of our construction using plausible physical assumptions for large-scale superconducting qubit platforms: a planar grid of qubits with nearest-neighbor connectivity, a characteristic physical gate error rate of $10^{-3}$, a surface code cycle time of 1 microsecond, and a reaction time of 10 microseconds. We account for factors that are normally ignored such as noise, the need to make repeated attempts, and the spacetime layout of the computation. When factoring 2048 bit RSA integers, our construction's spacetime volume is a hundredfold less than comparable estimates from earlier works (Van Meter et al. 2009, Jones et al. 2010, Fowler et al. 2012, Gheorghiu et al. 2019). In the abstract circuit model (which ignores overheads from distillation, routing, and error correction) our construction uses $3 n + 0.002 n \lg n$ logical qubits, $0.3 n^3 + 0.0005 n^3 \lg n$ Toffolis, and $500 n^2 + n^2 \lg n$ measurement depth to factor $n$-bit RSA integers. We quantify the cryptographic implications of our work, both for RSA and for schemes based on the DLP in finite fields.",https://arxiv.org/abs/1905.09749,"",,2024-02-07 02:50:27.442+00,2024-02-07 02:50:27.442+00,19,,2024-02-07 02:50:27.442+00,"","",211,Cryptography,Cryptography (Factoring),These are resource estimates for the scale of quantum computers necessary to do useful work in factoring encryption keys.,2024-02-07 02:40:18.386+00,2024-02-29 12:21:10.151+00,19,59,f,How to factor 2048 bit RSA integers in 8 hours using 20 million noisy qubits,761,Cryptography
+1682,f,T-count,48000000,2024-02-07 02:49:44.035+00,2024-02-07 02:49:44.035+00,,19,1050,1084,2023-06-14,"",,,,50000,,,,1084,2024-02-07 02:48:54.314+00,2024-02-07 02:48:54.314+00,,19,696,211,696,How to compute a 256-bit elliptic curve private key with only 50 million Toffoli gates,how to compute a 256-bit elliptic curve private key with only 50 million toffoli gates,"We use Shor's algorithm for the computation of elliptic curve private keys as a case study for resource estimates in the silicon-photonics-inspired active-volume architecture. Here, a fault-tolerant surface-code quantum computer consists of modules with a logarithmic number of non-local inter-module connections, modifying the algorithmic cost function compared to 2D-local architectures. We find that the non-local connections reduce the cost per key by a factor of 300-700 depending on the operating regime. At 10% threshold, assuming a 10-$μ$s code cycle and non-local connections, one key can be generated every 10 minutes using 6000 modules with 1152 physical qubits each. By contrast, a device with strict 2D-local connectivity requires more qubits and produces one key every 38 hours. We also find simple architecture-independent algorithmic modifications that reduce the Toffoli count per key by up to a factor of 5. These modifications involve reusing the stored state for multiple keys and spreading the cost of the modular division operation over multiple parallel instances of the algorithm.",https://arxiv.org/abs/2306.08585,"",2023-11-30 17:31:29.866+00,2023-10-21 13:24:33.838+00,2023-11-30 17:31:29.866+00,19,,2023-10-21 13:24:33.838+00,"","",211,Cryptography,Cryptography (Factoring),These are resource estimates for the scale of quantum computers necessary to do useful work in factoring encryption keys.,2024-02-07 02:40:18.386+00,2024-02-29 12:21:10.151+00,19,59,f,How to compute a 256-bit elliptic curve private key with only 50 million Toffoli gates,696,Cryptography
+1681,f,T-count,110000000,2024-02-07 02:49:25.721+00,2024-02-07 02:49:25.721+00,,19,1050,1084,2023-06-14,"",,,,3000,,,,1084,2024-02-07 02:48:54.314+00,2024-02-07 02:48:54.314+00,,19,696,211,696,How to compute a 256-bit elliptic curve private key with only 50 million Toffoli gates,how to compute a 256-bit elliptic curve private key with only 50 million toffoli gates,"We use Shor's algorithm for the computation of elliptic curve private keys as a case study for resource estimates in the silicon-photonics-inspired active-volume architecture. Here, a fault-tolerant surface-code quantum computer consists of modules with a logarithmic number of non-local inter-module connections, modifying the algorithmic cost function compared to 2D-local architectures. We find that the non-local connections reduce the cost per key by a factor of 300-700 depending on the operating regime. At 10% threshold, assuming a 10-$μ$s code cycle and non-local connections, one key can be generated every 10 minutes using 6000 modules with 1152 physical qubits each. By contrast, a device with strict 2D-local connectivity requires more qubits and produces one key every 38 hours. We also find simple architecture-independent algorithmic modifications that reduce the Toffoli count per key by up to a factor of 5. These modifications involve reusing the stored state for multiple keys and spreading the cost of the modular division operation over multiple parallel instances of the algorithm.",https://arxiv.org/abs/2306.08585,"",2023-11-30 17:31:29.866+00,2023-10-21 13:24:33.838+00,2023-11-30 17:31:29.866+00,19,,2023-10-21 13:24:33.838+00,"","",211,Cryptography,Cryptography (Factoring),These are resource estimates for the scale of quantum computers necessary to do useful work in factoring encryption keys.,2024-02-07 02:40:18.386+00,2024-02-29 12:21:10.151+00,19,59,f,How to compute a 256-bit elliptic curve private key with only 50 million Toffoli gates,696,Cryptography
+1680,f,T-count,17000000000,2024-02-07 02:47:35.997+00,2024-02-07 02:47:35.997+00,,19,1049,1083,2020-01-27,"",,,,5273,,,,1083,2024-02-07 02:44:52.194+00,2024-02-07 02:44:52.194+00,,19,760,211,760,Improved quantum circuits for elliptic curve discrete logarithms,improved quantum circuits for elliptic curve discrete logarithms,"We present improved quantum circuits for elliptic curve scalar multiplication, the most costly component in Shor's algorithm to compute discrete logarithms in elliptic curve groups. We optimize low-level components such as reversible integer and modular arithmetic through windowing techniques and more adaptive placement of uncomputing steps, and improve over previous quantum circuits for modular inversion by reformulating the binary Euclidean algorithm. Overall, we obtain an affine Weierstrass point addition circuit that has lower depth and uses fewer $T$ gates than previous circuits. While previous work mostly focuses on minimizing the total number of qubits, we present various trade-offs between different cost metrics including the number of qubits, circuit depth and $T$-gate count. Finally, we provide a full implementation of point addition in the Q# quantum programming language that allows unit tests and automatic quantum resource estimation for all components.",https://arxiv.org/abs/2001.09580,"",,2024-02-07 02:43:58.763+00,2024-02-07 02:43:58.763+00,19,,2024-02-07 02:43:58.762+00,"","",211,Cryptography,Cryptography (Factoring),These are resource estimates for the scale of quantum computers necessary to do useful work in factoring encryption keys.,2024-02-07 02:40:18.386+00,2024-02-29 12:21:10.151+00,19,59,f,Improved quantum circuits for elliptic curve discrete logarithms,760,Cryptography
+1679,f,T-count,62000000000,2024-02-07 02:47:13.484+00,2024-02-07 02:47:13.484+00,,19,1049,1083,2020-01-27,"",,,,4258,,,,1083,2024-02-07 02:44:52.194+00,2024-02-07 02:44:52.194+00,,19,760,211,760,Improved quantum circuits for elliptic curve discrete logarithms,improved quantum circuits for elliptic curve discrete logarithms,"We present improved quantum circuits for elliptic curve scalar multiplication, the most costly component in Shor's algorithm to compute discrete logarithms in elliptic curve groups. We optimize low-level components such as reversible integer and modular arithmetic through windowing techniques and more adaptive placement of uncomputing steps, and improve over previous quantum circuits for modular inversion by reformulating the binary Euclidean algorithm. Overall, we obtain an affine Weierstrass point addition circuit that has lower depth and uses fewer $T$ gates than previous circuits. While previous work mostly focuses on minimizing the total number of qubits, we present various trade-offs between different cost metrics including the number of qubits, circuit depth and $T$-gate count. Finally, we provide a full implementation of point addition in the Q# quantum programming language that allows unit tests and automatic quantum resource estimation for all components.",https://arxiv.org/abs/2001.09580,"",,2024-02-07 02:43:58.763+00,2024-02-07 02:43:58.763+00,19,,2024-02-07 02:43:58.762+00,"","",211,Cryptography,Cryptography (Factoring),These are resource estimates for the scale of quantum computers necessary to do useful work in factoring encryption keys.,2024-02-07 02:40:18.386+00,2024-02-29 12:21:10.151+00,19,59,f,Improved quantum circuits for elliptic curve discrete logarithms,760,Cryptography
+1678,f,T-count,7500000000,2024-02-07 02:46:50.94+00,2024-02-07 02:46:50.94+00,,19,1048,1083,2020-01-27,"",,,,3901,,,,1083,2024-02-07 02:44:52.194+00,2024-02-07 02:44:52.194+00,,19,760,211,760,Improved quantum circuits for elliptic curve discrete logarithms,improved quantum circuits for elliptic curve discrete logarithms,"We present improved quantum circuits for elliptic curve scalar multiplication, the most costly component in Shor's algorithm to compute discrete logarithms in elliptic curve groups. We optimize low-level components such as reversible integer and modular arithmetic through windowing techniques and more adaptive placement of uncomputing steps, and improve over previous quantum circuits for modular inversion by reformulating the binary Euclidean algorithm. Overall, we obtain an affine Weierstrass point addition circuit that has lower depth and uses fewer $T$ gates than previous circuits. While previous work mostly focuses on minimizing the total number of qubits, we present various trade-offs between different cost metrics including the number of qubits, circuit depth and $T$-gate count. Finally, we provide a full implementation of point addition in the Q# quantum programming language that allows unit tests and automatic quantum resource estimation for all components.",https://arxiv.org/abs/2001.09580,"",,2024-02-07 02:43:58.763+00,2024-02-07 02:43:58.763+00,19,,2024-02-07 02:43:58.762+00,"","",211,Cryptography,Cryptography (Factoring),These are resource estimates for the scale of quantum computers necessary to do useful work in factoring encryption keys.,2024-02-07 02:40:18.386+00,2024-02-29 12:21:10.151+00,19,59,f,Improved quantum circuits for elliptic curve discrete logarithms,760,Cryptography
+1677,f,T-count,26000000000,2024-02-07 02:46:27.065+00,2024-02-07 02:46:27.065+00,,19,1048,1083,2020-01-27,"",,,,3151,,,,1083,2024-02-07 02:44:52.194+00,2024-02-07 02:44:52.194+00,,19,760,211,760,Improved quantum circuits for elliptic curve discrete logarithms,improved quantum circuits for elliptic curve discrete logarithms,"We present improved quantum circuits for elliptic curve scalar multiplication, the most costly component in Shor's algorithm to compute discrete logarithms in elliptic curve groups. We optimize low-level components such as reversible integer and modular arithmetic through windowing techniques and more adaptive placement of uncomputing steps, and improve over previous quantum circuits for modular inversion by reformulating the binary Euclidean algorithm. Overall, we obtain an affine Weierstrass point addition circuit that has lower depth and uses fewer $T$ gates than previous circuits. While previous work mostly focuses on minimizing the total number of qubits, we present various trade-offs between different cost metrics including the number of qubits, circuit depth and $T$-gate count. Finally, we provide a full implementation of point addition in the Q# quantum programming language that allows unit tests and automatic quantum resource estimation for all components.",https://arxiv.org/abs/2001.09580,"",,2024-02-07 02:43:58.763+00,2024-02-07 02:43:58.763+00,19,,2024-02-07 02:43:58.762+00,"","",211,Cryptography,Cryptography (Factoring),These are resource estimates for the scale of quantum computers necessary to do useful work in factoring encryption keys.,2024-02-07 02:40:18.386+00,2024-02-29 12:21:10.151+00,19,59,f,Improved quantum circuits for elliptic curve discrete logarithms,760,Cryptography
+1676,f,T-count,2300000000,2024-02-07 02:45:55.587+00,2024-02-07 02:45:55.587+00,,19,1047,1083,2020-01-27,"",,,,2619,,,,1083,2024-02-07 02:44:52.194+00,2024-02-07 02:44:52.194+00,,19,760,211,760,Improved quantum circuits for elliptic curve discrete logarithms,improved quantum circuits for elliptic curve discrete logarithms,"We present improved quantum circuits for elliptic curve scalar multiplication, the most costly component in Shor's algorithm to compute discrete logarithms in elliptic curve groups. We optimize low-level components such as reversible integer and modular arithmetic through windowing techniques and more adaptive placement of uncomputing steps, and improve over previous quantum circuits for modular inversion by reformulating the binary Euclidean algorithm. Overall, we obtain an affine Weierstrass point addition circuit that has lower depth and uses fewer $T$ gates than previous circuits. While previous work mostly focuses on minimizing the total number of qubits, we present various trade-offs between different cost metrics including the number of qubits, circuit depth and $T$-gate count. Finally, we provide a full implementation of point addition in the Q# quantum programming language that allows unit tests and automatic quantum resource estimation for all components.",https://arxiv.org/abs/2001.09580,"",,2024-02-07 02:43:58.763+00,2024-02-07 02:43:58.763+00,19,,2024-02-07 02:43:58.762+00,"","",211,Cryptography,Cryptography (Factoring),These are resource estimates for the scale of quantum computers necessary to do useful work in factoring encryption keys.,2024-02-07 02:40:18.386+00,2024-02-29 12:21:10.151+00,19,59,f,Improved quantum circuits for elliptic curve discrete logarithms,760,Cryptography
+1675,f,T-count,7400000000,2024-02-07 02:45:23.6+00,2024-02-07 02:45:23.6+00,,19,1047,1083,2020-01-27,"",,,,2124,,,,1083,2024-02-07 02:44:52.194+00,2024-02-07 02:44:52.194+00,,19,760,211,760,Improved quantum circuits for elliptic curve discrete logarithms,improved quantum circuits for elliptic curve discrete logarithms,"We present improved quantum circuits for elliptic curve scalar multiplication, the most costly component in Shor's algorithm to compute discrete logarithms in elliptic curve groups. We optimize low-level components such as reversible integer and modular arithmetic through windowing techniques and more adaptive placement of uncomputing steps, and improve over previous quantum circuits for modular inversion by reformulating the binary Euclidean algorithm. Overall, we obtain an affine Weierstrass point addition circuit that has lower depth and uses fewer $T$ gates than previous circuits. While previous work mostly focuses on minimizing the total number of qubits, we present various trade-offs between different cost metrics including the number of qubits, circuit depth and $T$-gate count. Finally, we provide a full implementation of point addition in the Q# quantum programming language that allows unit tests and automatic quantum resource estimation for all components.",https://arxiv.org/abs/2001.09580,"",,2024-02-07 02:43:58.763+00,2024-02-07 02:43:58.763+00,19,,2024-02-07 02:43:58.762+00,"","",211,Cryptography,Cryptography (Factoring),These are resource estimates for the scale of quantum computers necessary to do useful work in factoring encryption keys.,2024-02-07 02:40:18.386+00,2024-02-29 12:21:10.151+00,19,59,f,Improved quantum circuits for elliptic curve discrete logarithms,760,Cryptography
+1674,f,T-count,1140000000000,2024-02-07 02:43:11.8+00,2024-02-07 02:43:11.8+00,,19,1046,1082,2017-06-21,"",,,,4719,,,,1082,2024-02-07 02:40:18.392+00,2024-02-07 02:40:18.392+00,,19,759,211,759,Quantum resource estimates for computing elliptic curve discrete logarithms,quantum resource estimates for computing elliptic curve discrete logarithms,"We give precise quantum resource estimates for Shor's algorithm to compute discrete logarithms on elliptic curves over prime fields. The estimates are derived from a simulation of a Toffoli gate network for controlled elliptic curve point addition, implemented within the framework of the quantum computing software tool suite LIQ$Ui|\rangle$. We determine circuit implementations for reversible modular arithmetic, including modular addition, multiplication and inversion, as well as reversible elliptic curve point addition. We conclude that elliptic curve discrete logarithms on an elliptic curve defined over an $n$-bit prime field can be computed on a quantum computer with at most $9n + 2\lceil\log_2(n)\rceil+10$ qubits using a quantum circuit of at most $448 n^3 \log_2(n) + 4090 n^3$ Toffoli gates. We are able to classically simulate the Toffoli networks corresponding to the controlled elliptic curve point addition as the core piece of Shor's algorithm for the NIST standard curves P-192, P-224, P-256, P-384 and P-521. Our approach allows gate-level comparisons to recent resource estimates for Shor's factoring algorithm. The results also support estimates given earlier by Proos and Zalka and indicate that, for current parameters at comparable classical security levels, the number of qubits required to tackle elliptic curves is less than for attacking RSA, suggesting that indeed ECC is an easier target than RSA.",https://arxiv.org/abs/1706.06752,"",,2024-02-07 02:39:04.956+00,2024-02-07 02:39:04.956+00,19,,2024-02-07 02:39:04.955+00,"","",211,Cryptography,Cryptography (Factoring),These are resource estimates for the scale of quantum computers necessary to do useful work in factoring encryption keys.,2024-02-07 02:40:18.386+00,2024-02-29 12:21:10.151+00,19,59,f,Quantum resource estimates for computing elliptic curve discrete logarithms,759,Cryptography
+1673,f,T-count,126000000000,2024-02-07 02:42:45.226+00,2024-02-07 02:42:45.226+00,,19,1045,1082,2017-06-21,"",,,,2330,,,,1082,2024-02-07 02:40:18.392+00,2024-02-07 02:40:18.392+00,,19,759,211,759,Quantum resource estimates for computing elliptic curve discrete logarithms,quantum resource estimates for computing elliptic curve discrete logarithms,"We give precise quantum resource estimates for Shor's algorithm to compute discrete logarithms on elliptic curves over prime fields. The estimates are derived from a simulation of a Toffoli gate network for controlled elliptic curve point addition, implemented within the framework of the quantum computing software tool suite LIQ$Ui|\rangle$. We determine circuit implementations for reversible modular arithmetic, including modular addition, multiplication and inversion, as well as reversible elliptic curve point addition. We conclude that elliptic curve discrete logarithms on an elliptic curve defined over an $n$-bit prime field can be computed on a quantum computer with at most $9n + 2\lceil\log_2(n)\rceil+10$ qubits using a quantum circuit of at most $448 n^3 \log_2(n) + 4090 n^3$ Toffoli gates. We are able to classically simulate the Toffoli networks corresponding to the controlled elliptic curve point addition as the core piece of Shor's algorithm for the NIST standard curves P-192, P-224, P-256, P-384 and P-521. Our approach allows gate-level comparisons to recent resource estimates for Shor's factoring algorithm. The results also support estimates given earlier by Proos and Zalka and indicate that, for current parameters at comparable classical security levels, the number of qubits required to tackle elliptic curves is less than for attacking RSA, suggesting that indeed ECC is an easier target than RSA.",https://arxiv.org/abs/1706.06752,"",,2024-02-07 02:39:04.956+00,2024-02-07 02:39:04.956+00,19,,2024-02-07 02:39:04.955+00,"","",211,Cryptography,Cryptography (Factoring),These are resource estimates for the scale of quantum computers necessary to do useful work in factoring encryption keys.,2024-02-07 02:40:18.386+00,2024-02-29 12:21:10.151+00,19,59,f,Quantum resource estimates for computing elliptic curve discrete logarithms,759,Cryptography
+1672,f,T-count,84300000000,2024-02-07 02:42:14.132+00,2024-02-07 02:42:14.132+00,,19,1043,1082,2017-06-21,"",,,,2042,,,,1082,2024-02-07 02:40:18.392+00,2024-02-07 02:40:18.392+00,,19,759,211,759,Quantum resource estimates for computing elliptic curve discrete logarithms,quantum resource estimates for computing elliptic curve discrete logarithms,"We give precise quantum resource estimates for Shor's algorithm to compute discrete logarithms on elliptic curves over prime fields. The estimates are derived from a simulation of a Toffoli gate network for controlled elliptic curve point addition, implemented within the framework of the quantum computing software tool suite LIQ$Ui|\rangle$. We determine circuit implementations for reversible modular arithmetic, including modular addition, multiplication and inversion, as well as reversible elliptic curve point addition. We conclude that elliptic curve discrete logarithms on an elliptic curve defined over an $n$-bit prime field can be computed on a quantum computer with at most $9n + 2\lceil\log_2(n)\rceil+10$ qubits using a quantum circuit of at most $448 n^3 \log_2(n) + 4090 n^3$ Toffoli gates. We are able to classically simulate the Toffoli networks corresponding to the controlled elliptic curve point addition as the core piece of Shor's algorithm for the NIST standard curves P-192, P-224, P-256, P-384 and P-521. Our approach allows gate-level comparisons to recent resource estimates for Shor's factoring algorithm. The results also support estimates given earlier by Proos and Zalka and indicate that, for current parameters at comparable classical security levels, the number of qubits required to tackle elliptic curves is less than for attacking RSA, suggesting that indeed ECC is an easier target than RSA.",https://arxiv.org/abs/1706.06752,"",,2024-02-07 02:39:04.956+00,2024-02-07 02:39:04.956+00,19,,2024-02-07 02:39:04.955+00,"","",211,Cryptography,Cryptography (Factoring),These are resource estimates for the scale of quantum computers necessary to do useful work in factoring encryption keys.,2024-02-07 02:40:18.386+00,2024-02-29 12:21:10.151+00,19,59,f,Quantum resource estimates for computing elliptic curve discrete logarithms,759,Cryptography
+1670,f,T-count,9440000000,2024-02-07 02:41:10.068+00,2024-02-07 02:41:10.068+00,,19,1043,1082,2017-06-21,"",,,,1014,,,,1082,2024-02-07 02:40:18.392+00,2024-02-07 02:40:18.392+00,,19,759,211,759,Quantum resource estimates for computing elliptic curve discrete logarithms,quantum resource estimates for computing elliptic curve discrete logarithms,"We give precise quantum resource estimates for Shor's algorithm to compute discrete logarithms on elliptic curves over prime fields. The estimates are derived from a simulation of a Toffoli gate network for controlled elliptic curve point addition, implemented within the framework of the quantum computing software tool suite LIQ$Ui|\rangle$. We determine circuit implementations for reversible modular arithmetic, including modular addition, multiplication and inversion, as well as reversible elliptic curve point addition. We conclude that elliptic curve discrete logarithms on an elliptic curve defined over an $n$-bit prime field can be computed on a quantum computer with at most $9n + 2\lceil\log_2(n)\rceil+10$ qubits using a quantum circuit of at most $448 n^3 \log_2(n) + 4090 n^3$ Toffoli gates. We are able to classically simulate the Toffoli networks corresponding to the controlled elliptic curve point addition as the core piece of Shor's algorithm for the NIST standard curves P-192, P-224, P-256, P-384 and P-521. Our approach allows gate-level comparisons to recent resource estimates for Shor's factoring algorithm. The results also support estimates given earlier by Proos and Zalka and indicate that, for current parameters at comparable classical security levels, the number of qubits required to tackle elliptic curves is less than for attacking RSA, suggesting that indeed ECC is an easier target than RSA.",https://arxiv.org/abs/1706.06752,"",,2024-02-07 02:39:04.956+00,2024-02-07 02:39:04.956+00,19,,2024-02-07 02:39:04.955+00,"","",211,Cryptography,Cryptography (Factoring),These are resource estimates for the scale of quantum computers necessary to do useful work in factoring encryption keys.,2024-02-07 02:40:18.386+00,2024-02-29 12:21:10.151+00,19,59,f,Quantum resource estimates for computing elliptic curve discrete logarithms,759,Cryptography
+1669,f,T-count,1000000000000,2024-02-07 02:36:27.379+00,2024-02-07 02:36:27.379+00,,19,1042,1081,2022-09-27,"n = 10000, k = 4",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1668,t,Qubits,10000,2024-02-07 02:36:11.081+00,2024-02-07 02:36:11.081+00,,19,1042,1081,2022-09-27,"n = 10000, k = 4",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1667,f,T-count,10000000000,2024-02-07 02:35:50.347+00,2024-02-07 02:35:50.347+00,,19,1042,1081,2022-09-27,"n = 1000, k = 4",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1666,t,Qubits,1000,2024-02-07 02:35:34.747+00,2024-02-07 02:35:34.747+00,,19,1042,1081,2022-09-27,"n = 1000, k = 4",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1665,f,T-count,100000000,2024-02-07 02:35:18.684+00,2024-02-07 02:35:18.684+00,,19,1042,1081,2022-09-27,"n = 100, k = 4",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1664,t,Qubits,100,2024-02-07 02:34:56.165+00,2024-02-07 02:34:56.165+00,,19,1042,1081,2022-09-27,"n = 100, k = 4",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1663,f,T-count,6800000000,2024-02-07 02:34:39.004+00,2024-02-07 02:34:39.004+00,,19,1042,1081,2022-09-27,"n = 256, k = 16",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1662,t,Qubits,256,2024-02-07 02:34:22.878+00,2024-02-07 02:34:22.878+00,,19,1042,1081,2022-09-27,"n = 256, k = 16",,,,,,,,1081,2024-02-07 02:33:51.428+00,2024-02-07 02:33:51.428+00,,19,758,124,758,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,analyzing prospects for quantum advantage in topological data analysis,"Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.",https://arxiv.org/abs/2209.13581,"",,2024-02-07 02:33:08.495+00,2024-02-07 02:33:08.495+00,19,,2024-02-07 02:33:08.494+00,"","",124,Machine learning,Machine learning,"Quantum computers can be used to implement routines or subroutines of machine learning algorithms. They generally belong to two broad categories: classical machine learning algorithm (that use hybrid quantum algorithms as subroutines) or proper quantum machine learning algorithms, e.g., that implement a quantum algorithm to learn features of quantum data.",2022-07-12 18:41:18.968+00,2023-06-02 14:47:32.856+00,39,59,f,Analyzing Prospects for Quantum Advantage in Topological Data Analysis,758,Machine learning
+1661,f,T-count,440000000000,2024-02-07 02:31:42.427+00,2024-02-07 02:31:42.427+00,,19,1041,1080,2023-12-26,Weak Euler scheme,,,,,,,,1080,2024-02-07 02:21:12.293+00,2024-02-07 02:21:12.293+00,,19,757,210,757,Option pricing under stochastic volatility on a quantum computer,option pricing under stochastic volatility on a quantum computer,"We develop quantum algorithms for pricing Asian and barrier options under the Heston model, a popular stochastic volatility model, and estimate their costs, in terms of T-count, T-depth and number of logical qubits, on instances under typical market conditions. These algorithms are based on combining well-established numerical methods for stochastic differential equations and quantum amplitude estimation technique. In particular, we empirically show that, despite its simplicity, weak Euler method achieves the same level of accuracy as the better-known strong Euler method in this task. Furthermore, by eliminating the expensive procedure of preparing Gaussian states, the quantum algorithm based on weak Euler scheme achieves drastically better efficiency than the one based on strong Euler scheme. Our resource analysis suggests that option pricing under stochastic volatility is a promising application of quantum computers, and that our algorithms render the hardware requirement for reaching practical quantum advantage in financial applications less stringent than prior art.",https://arxiv.org/abs/2312.15871,"",,2024-02-07 02:20:52.649+00,2024-02-07 02:20:52.649+00,19,,2024-02-07 02:20:52.648+00,"","",210,Finance,Finance (Derivative Pricing),These are resource estimates for the scale of quantum computers necessary to do useful work in pricing derivatives.,2024-02-07 00:22:03.206+00,2024-02-29 12:02:28.927+00,19,59,f,Option pricing under stochastic volatility on a quantum computer,757,Finance
+1660,t,Qubits,64000,2024-02-07 02:31:17.273+00,2024-02-07 02:31:17.273+00,,19,1041,1080,2023-12-26,Strong Euler scheme,,,,,,,,1080,2024-02-07 02:21:12.293+00,2024-02-07 02:21:12.293+00,,19,757,210,757,Option pricing under stochastic volatility on a quantum computer,option pricing under stochastic volatility on a quantum computer,"We develop quantum algorithms for pricing Asian and barrier options under the Heston model, a popular stochastic volatility model, and estimate their costs, in terms of T-count, T-depth and number of logical qubits, on instances under typical market conditions. These algorithms are based on combining well-established numerical methods for stochastic differential equations and quantum amplitude estimation technique. In particular, we empirically show that, despite its simplicity, weak Euler method achieves the same level of accuracy as the better-known strong Euler method in this task. Furthermore, by eliminating the expensive procedure of preparing Gaussian states, the quantum algorithm based on weak Euler scheme achieves drastically better efficiency than the one based on strong Euler scheme. Our resource analysis suggests that option pricing under stochastic volatility is a promising application of quantum computers, and that our algorithms render the hardware requirement for reaching practical quantum advantage in financial applications less stringent than prior art.",https://arxiv.org/abs/2312.15871,"",,2024-02-07 02:20:52.649+00,2024-02-07 02:20:52.649+00,19,,2024-02-07 02:20:52.648+00,"","",210,Finance,Finance (Derivative Pricing),These are resource estimates for the scale of quantum computers necessary to do useful work in pricing derivatives.,2024-02-07 00:22:03.206+00,2024-02-29 12:02:28.927+00,19,59,f,Option pricing under stochastic volatility on a quantum computer,757,Finance
+1659,f,T-count,770000000000000,2024-02-07 02:30:56.519+00,2024-02-07 02:30:56.519+00,,19,1041,1080,2023-12-26,Strong Euler scheme,,,,,,,,1080,2024-02-07 02:21:12.293+00,2024-02-07 02:21:12.293+00,,19,757,210,757,Option pricing under stochastic volatility on a quantum computer,option pricing under stochastic volatility on a quantum computer,"We develop quantum algorithms for pricing Asian and barrier options under the Heston model, a popular stochastic volatility model, and estimate their costs, in terms of T-count, T-depth and number of logical qubits, on instances under typical market conditions. These algorithms are based on combining well-established numerical methods for stochastic differential equations and quantum amplitude estimation technique. In particular, we empirically show that, despite its simplicity, weak Euler method achieves the same level of accuracy as the better-known strong Euler method in this task. Furthermore, by eliminating the expensive procedure of preparing Gaussian states, the quantum algorithm based on weak Euler scheme achieves drastically better efficiency than the one based on strong Euler scheme. Our resource analysis suggests that option pricing under stochastic volatility is a promising application of quantum computers, and that our algorithms render the hardware requirement for reaching practical quantum advantage in financial applications less stringent than prior art.",https://arxiv.org/abs/2312.15871,"",,2024-02-07 02:20:52.649+00,2024-02-07 02:20:52.649+00,19,,2024-02-07 02:20:52.648+00,"","",210,Finance,Finance (Derivative Pricing),These are resource estimates for the scale of quantum computers necessary to do useful work in pricing derivatives.,2024-02-07 00:22:03.206+00,2024-02-29 12:02:28.927+00,19,59,f,Option pricing under stochastic volatility on a quantum computer,757,Finance
+1658,t,Qubits,130000,2024-02-07 02:30:37.942+00,2024-02-07 02:30:37.942+00,,19,1041,1080,2023-12-26,Strong Euler scheme,,,,,,,,1080,2024-02-07 02:21:12.293+00,2024-02-07 02:21:12.293+00,,19,757,210,757,Option pricing under stochastic volatility on a quantum computer,option pricing under stochastic volatility on a quantum computer,"We develop quantum algorithms for pricing Asian and barrier options under the Heston model, a popular stochastic volatility model, and estimate their costs, in terms of T-count, T-depth and number of logical qubits, on instances under typical market conditions. These algorithms are based on combining well-established numerical methods for stochastic differential equations and quantum amplitude estimation technique. In particular, we empirically show that, despite its simplicity, weak Euler method achieves the same level of accuracy as the better-known strong Euler method in this task. Furthermore, by eliminating the expensive procedure of preparing Gaussian states, the quantum algorithm based on weak Euler scheme achieves drastically better efficiency than the one based on strong Euler scheme. Our resource analysis suggests that option pricing under stochastic volatility is a promising application of quantum computers, and that our algorithms render the hardware requirement for reaching practical quantum advantage in financial applications less stringent than prior art.",https://arxiv.org/abs/2312.15871,"",,2024-02-07 02:20:52.649+00,2024-02-07 02:20:52.649+00,19,,2024-02-07 02:20:52.648+00,"","",210,Finance,Finance (Derivative Pricing),These are resource estimates for the scale of quantum computers necessary to do useful work in pricing derivatives.,2024-02-07 00:22:03.206+00,2024-02-29 12:02:28.927+00,19,59,f,Option pricing under stochastic volatility on a quantum computer,757,Finance
+1657,f,T-count,240000000000,2024-02-07 02:30:04.581+00,2024-02-07 02:30:04.581+00,,19,1041,1080,2023-12-26,Weak Euler scheme,,,,,,,,1080,2024-02-07 02:21:12.293+00,2024-02-07 02:21:12.293+00,,19,757,210,757,Option pricing under stochastic volatility on a quantum computer,option pricing under stochastic volatility on a quantum computer,"We develop quantum algorithms for pricing Asian and barrier options under the Heston model, a popular stochastic volatility model, and estimate their costs, in terms of T-count, T-depth and number of logical qubits, on instances under typical market conditions. These algorithms are based on combining well-established numerical methods for stochastic differential equations and quantum amplitude estimation technique. In particular, we empirically show that, despite its simplicity, weak Euler method achieves the same level of accuracy as the better-known strong Euler method in this task. Furthermore, by eliminating the expensive procedure of preparing Gaussian states, the quantum algorithm based on weak Euler scheme achieves drastically better efficiency than the one based on strong Euler scheme. Our resource analysis suggests that option pricing under stochastic volatility is a promising application of quantum computers, and that our algorithms render the hardware requirement for reaching practical quantum advantage in financial applications less stringent than prior art.",https://arxiv.org/abs/2312.15871,"",,2024-02-07 02:20:52.649+00,2024-02-07 02:20:52.649+00,19,,2024-02-07 02:20:52.648+00,"","",210,Finance,Finance (Derivative Pricing),These are resource estimates for the scale of quantum computers necessary to do useful work in pricing derivatives.,2024-02-07 00:22:03.206+00,2024-02-29 12:02:28.927+00,19,59,f,Option pricing under stochastic volatility on a quantum computer,757,Finance
+1656,t,Qubits,22000,2024-02-07 02:29:48.138+00,2024-02-07 02:29:48.138+00,,19,1041,1080,2023-12-26,Weak Euler scheme,,,,,,,,1080,2024-02-07 02:21:12.293+00,2024-02-07 02:21:12.293+00,,19,757,210,757,Option pricing under stochastic volatility on a quantum computer,option pricing under stochastic volatility on a quantum computer,"We develop quantum algorithms for pricing Asian and barrier options under the Heston model, a popular stochastic volatility model, and estimate their costs, in terms of T-count, T-depth and number of logical qubits, on instances under typical market conditions. These algorithms are based on combining well-established numerical methods for stochastic differential equations and quantum amplitude estimation technique. In particular, we empirically show that, despite its simplicity, weak Euler method achieves the same level of accuracy as the better-known strong Euler method in this task. Furthermore, by eliminating the expensive procedure of preparing Gaussian states, the quantum algorithm based on weak Euler scheme achieves drastically better efficiency than the one based on strong Euler scheme. Our resource analysis suggests that option pricing under stochastic volatility is a promising application of quantum computers, and that our algorithms render the hardware requirement for reaching practical quantum advantage in financial applications less stringent than prior art.",https://arxiv.org/abs/2312.15871,"",,2024-02-07 02:20:52.649+00,2024-02-07 02:20:52.649+00,19,,2024-02-07 02:20:52.648+00,"","",210,Finance,Finance (Derivative Pricing),These are resource estimates for the scale of quantum computers necessary to do useful work in pricing derivatives.,2024-02-07 00:22:03.206+00,2024-02-29 12:02:28.927+00,19,59,f,Option pricing under stochastic volatility on a quantum computer,757,Finance
+1655,f,T-count,190000000000000,2024-02-07 02:29:00.305+00,2024-02-07 02:29:00.305+00,,19,1041,1080,2023-12-26,Strong Euler scheme,,,,,,,,1080,2024-02-07 02:21:12.293+00,2024-02-07 02:21:12.293+00,,19,757,210,757,Option pricing under stochastic volatility on a quantum computer,option pricing under stochastic volatility on a quantum computer,"We develop quantum algorithms for pricing Asian and barrier options under the Heston model, a popular stochastic volatility model, and estimate their costs, in terms of T-count, T-depth and number of logical qubits, on instances under typical market conditions. These algorithms are based on combining well-established numerical methods for stochastic differential equations and quantum amplitude estimation technique. In particular, we empirically show that, despite its simplicity, weak Euler method achieves the same level of accuracy as the better-known strong Euler method in this task. Furthermore, by eliminating the expensive procedure of preparing Gaussian states, the quantum algorithm based on weak Euler scheme achieves drastically better efficiency than the one based on strong Euler scheme. Our resource analysis suggests that option pricing under stochastic volatility is a promising application of quantum computers, and that our algorithms render the hardware requirement for reaching practical quantum advantage in financial applications less stringent than prior art.",https://arxiv.org/abs/2312.15871,"",,2024-02-07 02:20:52.649+00,2024-02-07 02:20:52.649+00,19,,2024-02-07 02:20:52.648+00,"","",210,Finance,Finance (Derivative Pricing),These are resource estimates for the scale of quantum computers necessary to do useful work in pricing derivatives.,2024-02-07 00:22:03.206+00,2024-02-29 12:02:28.927+00,19,59,f,Option pricing under stochastic volatility on a quantum computer,757,Finance
+1654,f,T-count,190000000000000,2024-02-07 02:21:54.342+00,2024-02-07 02:21:54.342+00,,19,1041,1080,2023-12-26,Strong Euler scheme,,,,,,,,1080,2024-02-07 02:21:12.293+00,2024-02-07 02:21:12.293+00,,19,757,210,757,Option pricing under stochastic volatility on a quantum computer,option pricing under stochastic volatility on a quantum computer,"We develop quantum algorithms for pricing Asian and barrier options under the Heston model, a popular stochastic volatility model, and estimate their costs, in terms of T-count, T-depth and number of logical qubits, on instances under typical market conditions. These algorithms are based on combining well-established numerical methods for stochastic differential equations and quantum amplitude estimation technique. In particular, we empirically show that, despite its simplicity, weak Euler method achieves the same level of accuracy as the better-known strong Euler method in this task. Furthermore, by eliminating the expensive procedure of preparing Gaussian states, the quantum algorithm based on weak Euler scheme achieves drastically better efficiency than the one based on strong Euler scheme. Our resource analysis suggests that option pricing under stochastic volatility is a promising application of quantum computers, and that our algorithms render the hardware requirement for reaching practical quantum advantage in financial applications less stringent than prior art.",https://arxiv.org/abs/2312.15871,"",,2024-02-07 02:20:52.649+00,2024-02-07 02:20:52.649+00,19,,2024-02-07 02:20:52.648+00,"","",210,Finance,Finance (Derivative Pricing),These are resource estimates for the scale of quantum computers necessary to do useful work in pricing derivatives.,2024-02-07 00:22:03.206+00,2024-02-29 12:02:28.927+00,19,59,f,Option pricing under stochastic volatility on a quantum computer,757,Finance
+1653,t,Qubits,38000,2024-02-07 02:21:36.9+00,2024-02-07 02:21:36.9+00,,19,1041,1080,2023-12-26,Strong Euler scheme,,,,,,,,1080,2024-02-07 02:21:12.293+00,2024-02-07 02:21:12.293+00,,19,757,210,757,Option pricing under stochastic volatility on a quantum computer,option pricing under stochastic volatility on a quantum computer,"We develop quantum algorithms for pricing Asian and barrier options under the Heston model, a popular stochastic volatility model, and estimate their costs, in terms of T-count, T-depth and number of logical qubits, on instances under typical market conditions. These algorithms are based on combining well-established numerical methods for stochastic differential equations and quantum amplitude estimation technique. In particular, we empirically show that, despite its simplicity, weak Euler method achieves the same level of accuracy as the better-known strong Euler method in this task. Furthermore, by eliminating the expensive procedure of preparing Gaussian states, the quantum algorithm based on weak Euler scheme achieves drastically better efficiency than the one based on strong Euler scheme. Our resource analysis suggests that option pricing under stochastic volatility is a promising application of quantum computers, and that our algorithms render the hardware requirement for reaching practical quantum advantage in financial applications less stringent than prior art.",https://arxiv.org/abs/2312.15871,"",,2024-02-07 02:20:52.649+00,2024-02-07 02:20:52.649+00,19,,2024-02-07 02:20:52.648+00,"","",210,Finance,Finance (Derivative Pricing),These are resource estimates for the scale of quantum computers necessary to do useful work in pricing derivatives.,2024-02-07 00:22:03.206+00,2024-02-29 12:02:28.927+00,19,59,f,Option pricing under stochastic volatility on a quantum computer,757,Finance
+1652,f,T-count,10000000,2024-02-07 02:19:41.218+00,2024-02-07 02:19:41.218+00,,19,1040,1079,2021-11-24,"",,,,,,,,1079,2024-02-07 02:19:09.108+00,2024-02-07 02:19:09.108+00,,19,698,210,698,Towards Quantum Advantage in Financial Market Risk using Quantum Gradient Algorithms,towards quantum advantage in financial market risk using quantum gradient algorithms,"We introduce a quantum algorithm to compute the market risk of financial derivatives. Previous work has shown that quantum amplitude estimation can accelerate derivative pricing quadratically in the target error and we extend this to a quadratic error scaling advantage in market risk computation. We show that employing quantum gradient estimation algorithms can deliver a further quadratic advantage in the number of the associated market sensitivities, usually called greeks. By numerically simulating the quantum gradient estimation algorithms on financial derivatives of practical interest, we demonstrate that not only can we successfully estimate the greeks in the examples studied, but that the resource requirements can be significantly lower in practice than what is expected by theoretical complexity bounds. This additional advantage in the computation of financial market risk lowers the estimated logical clock rate required for financial quantum advantage from Chakrabarti et al. [Quantum 5, 463 (2021)] by a factor of ~7, from 50MHz to 7MHz, even for a modest number of greeks by industry standards (four). Moreover, we show that if we have access to enough resources, the quantum algorithm can be parallelized across 60 QPUs, in which case the logical clock rate of each device required to achieve the same overall runtime as the serial execution would be ~100kHz. Throughout this work, we summarize and compare several different combinations of quantum and classical approaches that could be used for computing the market risk of financial derivatives.",https://arxiv.org/abs/2111.12509,"",2023-11-30 17:31:29.872+00,2023-10-21 13:25:49.197+00,2023-11-30 17:31:29.872+00,19,,2023-10-21 13:25:49.197+00,"","",210,Finance,Finance (Derivative Pricing),These are resource estimates for the scale of quantum computers necessary to do useful work in pricing derivatives.,2024-02-07 00:22:03.206+00,2024-02-29 12:02:28.927+00,19,59,f,Towards Quantum Advantage in Financial Market Risk using Quantum Gradient Algorithms,698,Finance
+1651,t,Qubits,12000,2024-02-07 02:19:22.78+00,2024-02-07 02:19:22.78+00,,19,1040,1079,2021-11-24,"",,,,,,,,1079,2024-02-07 02:19:09.108+00,2024-02-07 02:19:09.108+00,,19,698,210,698,Towards Quantum Advantage in Financial Market Risk using Quantum Gradient Algorithms,towards quantum advantage in financial market risk using quantum gradient algorithms,"We introduce a quantum algorithm to compute the market risk of financial derivatives. Previous work has shown that quantum amplitude estimation can accelerate derivative pricing quadratically in the target error and we extend this to a quadratic error scaling advantage in market risk computation. We show that employing quantum gradient estimation algorithms can deliver a further quadratic advantage in the number of the associated market sensitivities, usually called greeks. By numerically simulating the quantum gradient estimation algorithms on financial derivatives of practical interest, we demonstrate that not only can we successfully estimate the greeks in the examples studied, but that the resource requirements can be significantly lower in practice than what is expected by theoretical complexity bounds. This additional advantage in the computation of financial market risk lowers the estimated logical clock rate required for financial quantum advantage from Chakrabarti et al. [Quantum 5, 463 (2021)] by a factor of ~7, from 50MHz to 7MHz, even for a modest number of greeks by industry standards (four). Moreover, we show that if we have access to enough resources, the quantum algorithm can be parallelized across 60 QPUs, in which case the logical clock rate of each device required to achieve the same overall runtime as the serial execution would be ~100kHz. Throughout this work, we summarize and compare several different combinations of quantum and classical approaches that could be used for computing the market risk of financial derivatives.",https://arxiv.org/abs/2111.12509,"",2023-11-30 17:31:29.872+00,2023-10-21 13:25:49.197+00,2023-11-30 17:31:29.872+00,19,,2023-10-21 13:25:49.197+00,"","",210,Finance,Finance (Derivative Pricing),These are resource estimates for the scale of quantum computers necessary to do useful work in pricing derivatives.,2024-02-07 00:22:03.206+00,2024-02-29 12:02:28.927+00,19,59,f,Towards Quantum Advantage in Financial Market Risk using Quantum Gradient Algorithms,698,Finance
+1650,f,T-count,2400000000,2024-02-07 02:18:16.541+00,2024-02-07 02:18:16.541+00,,19,1039,1078,2023-07-26,"",,,,,,,,1078,2024-02-07 02:17:42.876+00,2024-02-07 02:17:42.876+00,,19,700,210,700,Derivative Pricing using Quantum Signal Processing,derivative pricing using quantum signal processing,"Pricing financial derivatives on quantum computers typically includes quantum arithmetic components which contribute heavily to the quantum resources required by the corresponding circuits. In this manuscript, we introduce a method based on Quantum Signal Processing (QSP) to encode financial derivative payoffs directly into quantum amplitudes, alleviating the quantum circuits from the burden of costly quantum arithmetic. Compared to current state-of-the-art approaches in the literature, we find that for derivative contracts of practical interest, the application of QSP significantly reduces the required resources across all metrics considered, most notably the total number of T-gates by $\sim 16$x and the number of logical qubits by $\sim 4$x. Additionally, we estimate that the logical clock rate needed for quantum advantage is also reduced by a factor of $\sim 5$x. Overall, we find that quantum advantage will require $4.7$k logical qubits, and quantum devices that can execute $10^9$ T-gates at a rate of $45$MHz. While in this work we focus specifically on the payoff component of the derivative pricing process where the method we present is most readily applicable, similar techniques can be employed to further reduce the resources in other applications, such as state preparation.",https://arxiv.org/abs/2307.14310,"",2023-11-30 17:31:29.878+00,2023-10-21 13:27:27.302+00,2023-11-30 17:31:29.878+00,19,,2023-10-21 13:27:27.301+00,"","",210,Finance,Finance (Derivative Pricing),These are resource estimates for the scale of quantum computers necessary to do useful work in pricing derivatives.,2024-02-07 00:22:03.206+00,2024-02-29 12:02:28.927+00,19,59,f,Derivative Pricing using Quantum Signal Processing,700,Finance
+1649,t,Qubits,4700,2024-02-07 02:17:55.504+00,2024-02-07 02:17:55.504+00,,19,1039,1078,2023-07-26,"",,,,,,,,1078,2024-02-07 02:17:42.876+00,2024-02-07 02:17:42.876+00,,19,700,210,700,Derivative Pricing using Quantum Signal Processing,derivative pricing using quantum signal processing,"Pricing financial derivatives on quantum computers typically includes quantum arithmetic components which contribute heavily to the quantum resources required by the corresponding circuits. In this manuscript, we introduce a method based on Quantum Signal Processing (QSP) to encode financial derivative payoffs directly into quantum amplitudes, alleviating the quantum circuits from the burden of costly quantum arithmetic. Compared to current state-of-the-art approaches in the literature, we find that for derivative contracts of practical interest, the application of QSP significantly reduces the required resources across all metrics considered, most notably the total number of T-gates by $\sim 16$x and the number of logical qubits by $\sim 4$x. Additionally, we estimate that the logical clock rate needed for quantum advantage is also reduced by a factor of $\sim 5$x. Overall, we find that quantum advantage will require $4.7$k logical qubits, and quantum devices that can execute $10^9$ T-gates at a rate of $45$MHz. While in this work we focus specifically on the payoff component of the derivative pricing process where the method we present is most readily applicable, similar techniques can be employed to further reduce the resources in other applications, such as state preparation.",https://arxiv.org/abs/2307.14310,"",2023-11-30 17:31:29.878+00,2023-10-21 13:27:27.302+00,2023-11-30 17:31:29.878+00,19,,2023-10-21 13:27:27.301+00,"","",210,Finance,Finance (Derivative Pricing),These are resource estimates for the scale of quantum computers necessary to do useful work in pricing derivatives.,2024-02-07 00:22:03.206+00,2024-02-29 12:02:28.927+00,19,59,f,Derivative Pricing using Quantum Signal Processing,700,Finance
+1648,f,T-count,9800000000,2024-02-07 00:27:59.001+00,2024-02-07 00:27:59.001+00,,19,1038,1077,2020-12-07,TARF option; Re-parameterization method,,,,,,,,1077,2024-02-07 00:25:35.38+00,2024-02-07 00:25:35.38+00,,19,697,210,697,A Threshold for Quantum Advantage in Derivative Pricing,a threshold for quantum advantage in derivative pricing,"We give an upper bound on the resources required for valuable quantum advantage in pricing derivatives. To do so, we give the first complete resource estimates for useful quantum derivative pricing, using autocallable and Target Accrual Redemption Forward (TARF) derivatives as benchmark use cases. We uncover blocking challenges in known approaches and introduce a new method for quantum derivative pricing - the re-parameterization method - that avoids them. This method combines pre-trained variational circuits with fault-tolerant quantum computing to dramatically reduce resource requirements. We find that the benchmark use cases we examine require 8k logical qubits and a T-depth of 54 million. We estimate that quantum advantage would require executing this program at the order of a second. While the resource requirements given here are out of reach of current systems, we hope they will provide a roadmap for further improvements in algorithms, implementations, and planned hardware architectures.",https://arxiv.org/abs/2012.03819,"",2023-11-30 17:31:29.868+00,2023-10-21 13:25:16.056+00,2023-11-30 17:31:29.868+00,19,,2023-10-21 13:25:16.056+00,"","",210,Finance,Finance (Derivative Pricing),These are resource estimates for the scale of quantum computers necessary to do useful work in pricing derivatives.,2024-02-07 00:22:03.206+00,2024-02-29 12:02:28.927+00,19,59,f,A Threshold for Quantum Advantage in Derivative Pricing,697,Finance
+1647,t,Qubits,11500,2024-02-07 00:27:42.257+00,2024-02-07 00:27:42.257+00,,19,1038,1077,2020-12-07,TARF option; Re-parameterization method,,,,,,,,1077,2024-02-07 00:25:35.38+00,2024-02-07 00:25:35.38+00,,19,697,210,697,A Threshold for Quantum Advantage in Derivative Pricing,a threshold for quantum advantage in derivative pricing,"We give an upper bound on the resources required for valuable quantum advantage in pricing derivatives. To do so, we give the first complete resource estimates for useful quantum derivative pricing, using autocallable and Target Accrual Redemption Forward (TARF) derivatives as benchmark use cases. We uncover blocking challenges in known approaches and introduce a new method for quantum derivative pricing - the re-parameterization method - that avoids them. This method combines pre-trained variational circuits with fault-tolerant quantum computing to dramatically reduce resource requirements. We find that the benchmark use cases we examine require 8k logical qubits and a T-depth of 54 million. We estimate that quantum advantage would require executing this program at the order of a second. While the resource requirements given here are out of reach of current systems, we hope they will provide a roadmap for further improvements in algorithms, implementations, and planned hardware architectures.",https://arxiv.org/abs/2012.03819,"",2023-11-30 17:31:29.868+00,2023-10-21 13:25:16.056+00,2023-11-30 17:31:29.868+00,19,,2023-10-21 13:25:16.056+00,"","",210,Finance,Finance (Derivative Pricing),These are resource estimates for the scale of quantum computers necessary to do useful work in pricing derivatives.,2024-02-07 00:22:03.206+00,2024-02-29 12:02:28.927+00,19,59,f,A Threshold for Quantum Advantage in Derivative Pricing,697,Finance
+1646,f,T-count,12000000000,2024-02-07 00:27:14.943+00,2024-02-07 00:27:14.943+00,,19,1038,1077,2020-12-07,auto-callable option; Re-parameterization method,,,,,,,,1077,2024-02-07 00:25:35.38+00,2024-02-07 00:25:35.38+00,,19,697,210,697,A Threshold for Quantum Advantage in Derivative Pricing,a threshold for quantum advantage in derivative pricing,"We give an upper bound on the resources required for valuable quantum advantage in pricing derivatives. To do so, we give the first complete resource estimates for useful quantum derivative pricing, using autocallable and Target Accrual Redemption Forward (TARF) derivatives as benchmark use cases. We uncover blocking challenges in known approaches and introduce a new method for quantum derivative pricing - the re-parameterization method - that avoids them. This method combines pre-trained variational circuits with fault-tolerant quantum computing to dramatically reduce resource requirements. We find that the benchmark use cases we examine require 8k logical qubits and a T-depth of 54 million. We estimate that quantum advantage would require executing this program at the order of a second. While the resource requirements given here are out of reach of current systems, we hope they will provide a roadmap for further improvements in algorithms, implementations, and planned hardware architectures.",https://arxiv.org/abs/2012.03819,"",2023-11-30 17:31:29.868+00,2023-10-21 13:25:16.056+00,2023-11-30 17:31:29.868+00,19,,2023-10-21 13:25:16.056+00,"","",210,Finance,Finance (Derivative Pricing),These are resource estimates for the scale of quantum computers necessary to do useful work in pricing derivatives.,2024-02-07 00:22:03.206+00,2024-02-29 12:02:28.927+00,19,59,f,A Threshold for Quantum Advantage in Derivative Pricing,697,Finance
+1645,t,Qubits,8000,2024-02-07 00:26:47.343+00,2024-02-07 00:26:47.343+00,,19,1038,1077,2020-12-07,auto-callable option; Re-parameterization method,,,,,,,,1077,2024-02-07 00:25:35.38+00,2024-02-07 00:25:35.38+00,,19,697,210,697,A Threshold for Quantum Advantage in Derivative Pricing,a threshold for quantum advantage in derivative pricing,"We give an upper bound on the resources required for valuable quantum advantage in pricing derivatives. To do so, we give the first complete resource estimates for useful quantum derivative pricing, using autocallable and Target Accrual Redemption Forward (TARF) derivatives as benchmark use cases. We uncover blocking challenges in known approaches and introduce a new method for quantum derivative pricing - the re-parameterization method - that avoids them. This method combines pre-trained variational circuits with fault-tolerant quantum computing to dramatically reduce resource requirements. We find that the benchmark use cases we examine require 8k logical qubits and a T-depth of 54 million. We estimate that quantum advantage would require executing this program at the order of a second. While the resource requirements given here are out of reach of current systems, we hope they will provide a roadmap for further improvements in algorithms, implementations, and planned hardware architectures.",https://arxiv.org/abs/2012.03819,"",2023-11-30 17:31:29.868+00,2023-10-21 13:25:16.056+00,2023-11-30 17:31:29.868+00,19,,2023-10-21 13:25:16.056+00,"","",210,Finance,Finance (Derivative Pricing),These are resource estimates for the scale of quantum computers necessary to do useful work in pricing derivatives.,2024-02-07 00:22:03.206+00,2024-02-29 12:02:28.927+00,19,59,f,A Threshold for Quantum Advantage in Derivative Pricing,697,Finance
+1644,f,T-count,2e+24,2024-02-07 00:24:43.081+00,2024-02-07 00:24:43.081+00,,19,1036,1076,2023-10-04,N = 100 interior point method,,,,,,,,1076,2024-02-07 00:23:52.875+00,2024-02-07 00:23:52.875+00,,19,756,210,756,Quantum algorithms: A survey of applications and end-to-end complexities,quantum algorithms: a survey of applications and end-to-end complexities,"The anticipated applications of quantum computers span across science and industry, ranging from quantum chemistry and many-body physics to optimization, finance, and machine learning. Proposed quantum solutions in these areas typically combine multiple quantum algorithmic primitives into an overall quantum algorithm, which must then incorporate the methods of quantum error correction and fault tolerance to be implemented correctly on quantum hardware. As such, it can be difficult to assess how much a particular application benefits from quantum computing, as the various approaches are often sensitive to intricate technical details about the underlying primitives and their complexities. Here we present a survey of several potential application areas of quantum algorithms and their underlying algorithmic primitives, carefully considering technical caveats and subtleties. We outline the challenges and opportunities in each area in an ""end-to-end"" fashion by clearly defining the problem being solved alongside the input-output model, instantiating all ""oracles,"" and spelling out all hidden costs. We also compare quantum solutions against state-of-the-art classical methods and complexity-theoretic limitations to evaluate possible quantum speedups.   The survey is written in a modular, wiki-like fashion to facilitate navigation of the content. Each primitive and application area is discussed in a standalone section, with its own bibliography of references and embedded hyperlinks that direct to other relevant sections. This structure mirrors that of complex quantum algorithms that involve several layers of abstraction, and it enables rapid evaluation of how end-to-end complexities are impacted when subroutines are altered.",https://arxiv.org/abs/2310.03011,"",,2024-02-07 00:23:26.83+00,2024-02-07 00:23:26.83+00,19,,2024-02-07 00:23:26.83+00,"","",210,Finance,Finance (Derivative Pricing),These are resource estimates for the scale of quantum computers necessary to do useful work in pricing derivatives.,2024-02-07 00:22:03.206+00,2024-02-29 12:02:28.927+00,19,59,f,Quantum algorithms: A survey of applications and end-to-end complexities,756,Finance
+1643,t,Qubits,8000000,2024-02-07 00:24:20.929+00,2024-02-07 00:24:20.929+00,,19,1036,1076,2023-10-04,N = 100 interior point method,,,,,,,,1076,2024-02-07 00:23:52.875+00,2024-02-07 00:23:52.875+00,,19,756,210,756,Quantum algorithms: A survey of applications and end-to-end complexities,quantum algorithms: a survey of applications and end-to-end complexities,"The anticipated applications of quantum computers span across science and industry, ranging from quantum chemistry and many-body physics to optimization, finance, and machine learning. Proposed quantum solutions in these areas typically combine multiple quantum algorithmic primitives into an overall quantum algorithm, which must then incorporate the methods of quantum error correction and fault tolerance to be implemented correctly on quantum hardware. As such, it can be difficult to assess how much a particular application benefits from quantum computing, as the various approaches are often sensitive to intricate technical details about the underlying primitives and their complexities. Here we present a survey of several potential application areas of quantum algorithms and their underlying algorithmic primitives, carefully considering technical caveats and subtleties. We outline the challenges and opportunities in each area in an ""end-to-end"" fashion by clearly defining the problem being solved alongside the input-output model, instantiating all ""oracles,"" and spelling out all hidden costs. We also compare quantum solutions against state-of-the-art classical methods and complexity-theoretic limitations to evaluate possible quantum speedups.   The survey is written in a modular, wiki-like fashion to facilitate navigation of the content. Each primitive and application area is discussed in a standalone section, with its own bibliography of references and embedded hyperlinks that direct to other relevant sections. This structure mirrors that of complex quantum algorithms that involve several layers of abstraction, and it enables rapid evaluation of how end-to-end complexities are impacted when subroutines are altered.",https://arxiv.org/abs/2310.03011,"",,2024-02-07 00:23:26.83+00,2024-02-07 00:23:26.83+00,19,,2024-02-07 00:23:26.83+00,"","",210,Finance,Finance (Derivative Pricing),These are resource estimates for the scale of quantum computers necessary to do useful work in pricing derivatives.,2024-02-07 00:22:03.206+00,2024-02-29 12:02:28.927+00,19,59,f,Quantum algorithms: A survey of applications and end-to-end complexities,756,Finance
+1642,f,T-count,36000000,2024-02-07 00:22:46.283+00,2024-02-07 00:22:46.283+00,,19,1035,1075,2019-07-05,"",,,,,,,,1075,2024-02-07 00:22:03.21+00,2024-02-07 00:22:03.21+00,,19,755,210,755,Credit Risk Analysis using Quantum Computers,credit risk analysis using quantum computers,"We present and analyze a quantum algorithm to estimate credit risk more efficiently than Monte Carlo simulations can do on classical computers. More precisely, we estimate the economic capital requirement, i.e. the difference between the Value at Risk and the expected value of a given loss distribution. The economic capital requirement is an important risk metric because it summarizes the amount of capital required to remain solvent at a given confidence level. We implement this problem for a realistic loss distribution and analyze its scaling to a realistic problem size. In particular, we provide estimates of the total number of required qubits, the expected circuit depth, and how this translates into an expected runtime under reasonable assumptions on future fault-tolerant quantum hardware.",https://arxiv.org/abs/1907.03044,"",,2024-02-07 00:20:54.682+00,2024-02-07 00:20:54.682+00,19,,2024-02-07 00:20:54.682+00,"","",210,Finance,Finance (Derivative Pricing),These are resource estimates for the scale of quantum computers necessary to do useful work in pricing derivatives.,2024-02-07 00:22:03.206+00,2024-02-29 12:02:28.927+00,19,59,f,Credit Risk Analysis using Quantum Computers,755,Finance
+1641,t,Qubits,1000000,2024-02-07 00:22:25.192+00,2024-02-07 00:22:25.192+00,,19,1035,1075,2019-07-05,"",,,,,,,,1075,2024-02-07 00:22:03.21+00,2024-02-07 00:22:03.21+00,,19,755,210,755,Credit Risk Analysis using Quantum Computers,credit risk analysis using quantum computers,"We present and analyze a quantum algorithm to estimate credit risk more efficiently than Monte Carlo simulations can do on classical computers. More precisely, we estimate the economic capital requirement, i.e. the difference between the Value at Risk and the expected value of a given loss distribution. The economic capital requirement is an important risk metric because it summarizes the amount of capital required to remain solvent at a given confidence level. We implement this problem for a realistic loss distribution and analyze its scaling to a realistic problem size. In particular, we provide estimates of the total number of required qubits, the expected circuit depth, and how this translates into an expected runtime under reasonable assumptions on future fault-tolerant quantum hardware.",https://arxiv.org/abs/1907.03044,"",,2024-02-07 00:20:54.682+00,2024-02-07 00:20:54.682+00,19,,2024-02-07 00:20:54.682+00,"","",210,Finance,Finance (Derivative Pricing),These are resource estimates for the scale of quantum computers necessary to do useful work in pricing derivatives.,2024-02-07 00:22:03.206+00,2024-02-29 12:02:28.927+00,19,59,f,Credit Risk Analysis using Quantum Computers,755,Finance
+1640,f,T-count,2.0299999999999997e+21,2024-02-07 00:19:59.083+00,2024-02-07 00:19:59.083+00,,19,1034,1074,2023-03-24,"",,,,,,,,1074,2024-02-07 00:17:12.682+00,2024-02-07 00:17:12.682+00,,19,754,139,754,Fault-tolerant quantum computation of molecular observables,fault-tolerant quantum computation of molecular observables,"Over the past three decades significant reductions have been made to the cost of estimating ground-state energies of molecular Hamiltonians with quantum computers. However, comparatively little attention has been paid to estimating the expectation values of other observables with respect to said ground states, which is important for many industrial applications. In this work we present a novel expectation value estimation (EVE) quantum algorithm which can be applied to estimate the expectation values of arbitrary observables with respect to any of the system's eigenstates. In particular, we consider two variants of EVE: std-EVE, based on standard quantum phase estimation, and QSP-EVE, which utilizes quantum signal processing (QSP) techniques. We provide rigorous error analysis for both both variants and minimize the number of individual phase factors for QSPEVE. These error analyses enable us to produce constant-factor quantum resource estimates for both std-EVE and QSP-EVE across a variety of molecular systems and observables. For the systems considered, we show that QSP-EVE reduces (Toffoli) gate counts by up to three orders of magnitude and reduces qubit width by up to 25% compared to std-EVE. While estimated resource counts remain far too high for the first generations of fault-tolerant quantum computers, our estimates mark a first of their kind for both the application of expectation value estimation and modern QSP-based techniques.",https://arxiv.org/abs/2303.14118,"",,2024-02-07 00:16:45.411+00,2024-02-07 00:16:45.411+00,19,,2024-02-07 00:16:45.411+00,"","",139,Quantum computational chemistry (QCC),Quantum computational chemistry (QCC),the use of quantum computers to solve problems in computational quantum chemistry.,2022-07-14 12:21:27.924+00,2022-07-14 12:21:27.924+00,39,58,f,Fault-tolerant quantum computation of molecular observables,754,Quantum computational chemistry (QCC)
+1639,t,Qubits,7577,2024-02-07 00:19:33.757+00,2024-02-07 00:19:33.757+00,,19,1034,1074,2023-03-24,"",,,,,,,,1074,2024-02-07 00:17:12.682+00,2024-02-07 00:17:12.682+00,,19,754,139,754,Fault-tolerant quantum computation of molecular observables,fault-tolerant quantum computation of molecular observables,"Over the past three decades significant reductions have been made to the cost of estimating ground-state energies of molecular Hamiltonians with quantum computers. However, comparatively little attention has been paid to estimating the expectation values of other observables with respect to said ground states, which is important for many industrial applications. In this work we present a novel expectation value estimation (EVE) quantum algorithm which can be applied to estimate the expectation values of arbitrary observables with respect to any of the system's eigenstates. In particular, we consider two variants of EVE: std-EVE, based on standard quantum phase estimation, and QSP-EVE, which utilizes quantum signal processing (QSP) techniques. We provide rigorous error analysis for both both variants and minimize the number of individual phase factors for QSPEVE. These error analyses enable us to produce constant-factor quantum resource estimates for both std-EVE and QSP-EVE across a variety of molecular systems and observables. For the systems considered, we show that QSP-EVE reduces (Toffoli) gate counts by up to three orders of magnitude and reduces qubit width by up to 25% compared to std-EVE. While estimated resource counts remain far too high for the first generations of fault-tolerant quantum computers, our estimates mark a first of their kind for both the application of expectation value estimation and modern QSP-based techniques.",https://arxiv.org/abs/2303.14118,"",,2024-02-07 00:16:45.411+00,2024-02-07 00:16:45.411+00,19,,2024-02-07 00:16:45.411+00,"","",139,Quantum computational chemistry (QCC),Quantum computational chemistry (QCC),the use of quantum computers to solve problems in computational quantum chemistry.,2022-07-14 12:21:27.924+00,2022-07-14 12:21:27.924+00,39,58,f,Fault-tolerant quantum computation of molecular observables,754,Quantum computational chemistry (QCC)
+1638,f,T-count,1.92e+18,2024-02-07 00:19:15.706+00,2024-02-07 00:19:15.706+00,,19,1033,1074,2023-03-24,"",,,,,,,,1074,2024-02-07 00:17:12.682+00,2024-02-07 00:17:12.682+00,,19,754,139,754,Fault-tolerant quantum computation of molecular observables,fault-tolerant quantum computation of molecular observables,"Over the past three decades significant reductions have been made to the cost of estimating ground-state energies of molecular Hamiltonians with quantum computers. However, comparatively little attention has been paid to estimating the expectation values of other observables with respect to said ground states, which is important for many industrial applications. In this work we present a novel expectation value estimation (EVE) quantum algorithm which can be applied to estimate the expectation values of arbitrary observables with respect to any of the system's eigenstates. In particular, we consider two variants of EVE: std-EVE, based on standard quantum phase estimation, and QSP-EVE, which utilizes quantum signal processing (QSP) techniques. We provide rigorous error analysis for both both variants and minimize the number of individual phase factors for QSPEVE. These error analyses enable us to produce constant-factor quantum resource estimates for both std-EVE and QSP-EVE across a variety of molecular systems and observables. For the systems considered, we show that QSP-EVE reduces (Toffoli) gate counts by up to three orders of magnitude and reduces qubit width by up to 25% compared to std-EVE. While estimated resource counts remain far too high for the first generations of fault-tolerant quantum computers, our estimates mark a first of their kind for both the application of expectation value estimation and modern QSP-based techniques.",https://arxiv.org/abs/2303.14118,"",,2024-02-07 00:16:45.411+00,2024-02-07 00:16:45.411+00,19,,2024-02-07 00:16:45.411+00,"","",139,Quantum computational chemistry (QCC),Quantum computational chemistry (QCC),the use of quantum computers to solve problems in computational quantum chemistry.,2022-07-14 12:21:27.924+00,2022-07-14 12:21:27.924+00,39,58,f,Fault-tolerant quantum computation of molecular observables,754,Quantum computational chemistry (QCC)
+1637,t,Qubits,2130,2024-02-07 00:18:54.04+00,2024-02-07 00:18:54.04+00,,19,1033,1074,2023-03-24,"",,,,,,,,1074,2024-02-07 00:17:12.682+00,2024-02-07 00:17:12.682+00,,19,754,139,754,Fault-tolerant quantum computation of molecular observables,fault-tolerant quantum computation of molecular observables,"Over the past three decades significant reductions have been made to the cost of estimating ground-state energies of molecular Hamiltonians with quantum computers. However, comparatively little attention has been paid to estimating the expectation values of other observables with respect to said ground states, which is important for many industrial applications. In this work we present a novel expectation value estimation (EVE) quantum algorithm which can be applied to estimate the expectation values of arbitrary observables with respect to any of the system's eigenstates. In particular, we consider two variants of EVE: std-EVE, based on standard quantum phase estimation, and QSP-EVE, which utilizes quantum signal processing (QSP) techniques. We provide rigorous error analysis for both both variants and minimize the number of individual phase factors for QSPEVE. These error analyses enable us to produce constant-factor quantum resource estimates for both std-EVE and QSP-EVE across a variety of molecular systems and observables. For the systems considered, we show that QSP-EVE reduces (Toffoli) gate counts by up to three orders of magnitude and reduces qubit width by up to 25% compared to std-EVE. While estimated resource counts remain far too high for the first generations of fault-tolerant quantum computers, our estimates mark a first of their kind for both the application of expectation value estimation and modern QSP-based techniques.",https://arxiv.org/abs/2303.14118,"",,2024-02-07 00:16:45.411+00,2024-02-07 00:16:45.411+00,19,,2024-02-07 00:16:45.411+00,"","",139,Quantum computational chemistry (QCC),Quantum computational chemistry (QCC),the use of quantum computers to solve problems in computational quantum chemistry.,2022-07-14 12:21:27.924+00,2022-07-14 12:21:27.924+00,39,58,f,Fault-tolerant quantum computation of molecular observables,754,Quantum computational chemistry (QCC)
+1636,f,T-count,7.72e+17,2024-02-07 00:18:38.771+00,2024-02-07 00:18:38.771+00,,19,1032,1074,2023-03-24,"",,,,,,,,1074,2024-02-07 00:17:12.682+00,2024-02-07 00:17:12.682+00,,19,754,139,754,Fault-tolerant quantum computation of molecular observables,fault-tolerant quantum computation of molecular observables,"Over the past three decades significant reductions have been made to the cost of estimating ground-state energies of molecular Hamiltonians with quantum computers. However, comparatively little attention has been paid to estimating the expectation values of other observables with respect to said ground states, which is important for many industrial applications. In this work we present a novel expectation value estimation (EVE) quantum algorithm which can be applied to estimate the expectation values of arbitrary observables with respect to any of the system's eigenstates. In particular, we consider two variants of EVE: std-EVE, based on standard quantum phase estimation, and QSP-EVE, which utilizes quantum signal processing (QSP) techniques. We provide rigorous error analysis for both both variants and minimize the number of individual phase factors for QSPEVE. These error analyses enable us to produce constant-factor quantum resource estimates for both std-EVE and QSP-EVE across a variety of molecular systems and observables. For the systems considered, we show that QSP-EVE reduces (Toffoli) gate counts by up to three orders of magnitude and reduces qubit width by up to 25% compared to std-EVE. While estimated resource counts remain far too high for the first generations of fault-tolerant quantum computers, our estimates mark a first of their kind for both the application of expectation value estimation and modern QSP-based techniques.",https://arxiv.org/abs/2303.14118,"",,2024-02-07 00:16:45.411+00,2024-02-07 00:16:45.411+00,19,,2024-02-07 00:16:45.411+00,"","",139,Quantum computational chemistry (QCC),Quantum computational chemistry (QCC),the use of quantum computers to solve problems in computational quantum chemistry.,2022-07-14 12:21:27.924+00,2022-07-14 12:21:27.924+00,39,58,f,Fault-tolerant quantum computation of molecular observables,754,Quantum computational chemistry (QCC)
+1635,t,Qubits,1790,2024-02-07 00:18:18.479+00,2024-02-07 00:18:18.479+00,,19,1032,1074,2023-03-24,"",,,,,,,,1074,2024-02-07 00:17:12.682+00,2024-02-07 00:17:12.682+00,,19,754,139,754,Fault-tolerant quantum computation of molecular observables,fault-tolerant quantum computation of molecular observables,"Over the past three decades significant reductions have been made to the cost of estimating ground-state energies of molecular Hamiltonians with quantum computers. However, comparatively little attention has been paid to estimating the expectation values of other observables with respect to said ground states, which is important for many industrial applications. In this work we present a novel expectation value estimation (EVE) quantum algorithm which can be applied to estimate the expectation values of arbitrary observables with respect to any of the system's eigenstates. In particular, we consider two variants of EVE: std-EVE, based on standard quantum phase estimation, and QSP-EVE, which utilizes quantum signal processing (QSP) techniques. We provide rigorous error analysis for both both variants and minimize the number of individual phase factors for QSPEVE. These error analyses enable us to produce constant-factor quantum resource estimates for both std-EVE and QSP-EVE across a variety of molecular systems and observables. For the systems considered, we show that QSP-EVE reduces (Toffoli) gate counts by up to three orders of magnitude and reduces qubit width by up to 25% compared to std-EVE. While estimated resource counts remain far too high for the first generations of fault-tolerant quantum computers, our estimates mark a first of their kind for both the application of expectation value estimation and modern QSP-based techniques.",https://arxiv.org/abs/2303.14118,"",,2024-02-07 00:16:45.411+00,2024-02-07 00:16:45.411+00,19,,2024-02-07 00:16:45.411+00,"","",139,Quantum computational chemistry (QCC),Quantum computational chemistry (QCC),the use of quantum computers to solve problems in computational quantum chemistry.,2022-07-14 12:21:27.924+00,2022-07-14 12:21:27.924+00,39,58,f,Fault-tolerant quantum computation of molecular observables,754,Quantum computational chemistry (QCC)
+1634,f,T-count,11000000000,2024-02-07 00:16:01.486+00,2024-02-07 00:16:01.486+00,,19,1030,1073,2022-06-01,"",,,,,,,,1073,2024-02-07 00:15:14.177+00,2024-02-07 00:15:14.177+00,,19,753,139,753,A perspective on the current state-of-the-art of quantum computing for drug discovery applications,a perspective on the current state-of-the-art of quantum computing for drug discovery applications,"Computational chemistry is an essential tool in the pharmaceutical industry. Quantum computing is a fast evolving technology that promises to completely shift the computational capabilities in many areas of chemical research by bringing into reach currently impossible calculations. This perspective illustrates the near-future applicability of quantum computation to pharmaceutical problems. We briefly summarize and compare the scaling properties of state-of-the-art quantum algorithms, and provide novel estimates of the quantum computational cost of simulating progressively larger embedding regions of a pharmaceutically relevant covalent protein-drug complex involving the drug Ibrutinib. Carrying out these calculations requires an error-corrected quantum architecture, that we describe. Our estimates showcase that recent developments on quantum algorithms have dramatically reduced the quantum resources needed to run fully quantum calculations in active spaces of around 50 orbitals and electrons, from estimated over 1000 years using the Trotterisation approach to just a few days with sparse qubitisation, painting a picture of fast and exciting progress in this nascent field.",https://arxiv.org/abs/2206.00551,"",,2024-02-07 00:14:48.159+00,2024-02-07 00:14:48.159+00,19,,2024-02-07 00:14:48.159+00,"","",139,Quantum computational chemistry (QCC),Quantum computational chemistry (QCC),the use of quantum computers to solve problems in computational quantum chemistry.,2022-07-14 12:21:27.924+00,2022-07-14 12:21:27.924+00,39,58,f,A perspective on the current state-of-the-art of quantum computing for drug discovery applications,753,Quantum computational chemistry (QCC)
+1633,t,Qubits,2207,2024-02-07 00:15:43.186+00,2024-02-07 00:15:43.186+00,,19,1030,1073,2022-06-01,"",,,,,,,,1073,2024-02-07 00:15:14.177+00,2024-02-07 00:15:14.177+00,,19,753,139,753,A perspective on the current state-of-the-art of quantum computing for drug discovery applications,a perspective on the current state-of-the-art of quantum computing for drug discovery applications,"Computational chemistry is an essential tool in the pharmaceutical industry. Quantum computing is a fast evolving technology that promises to completely shift the computational capabilities in many areas of chemical research by bringing into reach currently impossible calculations. This perspective illustrates the near-future applicability of quantum computation to pharmaceutical problems. We briefly summarize and compare the scaling properties of state-of-the-art quantum algorithms, and provide novel estimates of the quantum computational cost of simulating progressively larger embedding regions of a pharmaceutically relevant covalent protein-drug complex involving the drug Ibrutinib. Carrying out these calculations requires an error-corrected quantum architecture, that we describe. Our estimates showcase that recent developments on quantum algorithms have dramatically reduced the quantum resources needed to run fully quantum calculations in active spaces of around 50 orbitals and electrons, from estimated over 1000 years using the Trotterisation approach to just a few days with sparse qubitisation, painting a picture of fast and exciting progress in this nascent field.",https://arxiv.org/abs/2206.00551,"",,2024-02-07 00:14:48.159+00,2024-02-07 00:14:48.159+00,19,,2024-02-07 00:14:48.159+00,"","",139,Quantum computational chemistry (QCC),Quantum computational chemistry (QCC),the use of quantum computers to solve problems in computational quantum chemistry.,2022-07-14 12:21:27.924+00,2022-07-14 12:21:27.924+00,39,58,f,A perspective on the current state-of-the-art of quantum computing for drug discovery applications,753,Quantum computational chemistry (QCC)
+1632,f,T-count,8000000000,2024-02-07 00:09:27.668+00,2024-02-07 00:09:27.668+00,,19,1029,1072,2020-09-25,"",,,,,,,,1072,2024-02-07 00:08:42.595+00,2024-02-07 00:08:42.595+00,,19,752,139,752,How will quantum computers provide an industrially relevant computational advantage in quantum chemistry?,how will quantum computers provide an industrially relevant computational advantage in quantum chemistry?,"Numerous reports claim that quantum advantage, which should emerge as a direct consequence of the advent of quantum computers, will herald a new era of chemical research because it will enable scientists to perform the kinds of quantum chemical simulations that have not been possible before. Such simulations on quantum computers, promising a significantly greater accuracy and speed, are projected to exert a great impact on the way we can probe reality, predict the outcomes of chemical experiments, and even drive design of drugs, catalysts, and materials. In this work we review the current status of quantum hardware and algorithm theory and examine whether such popular claims about quantum advantage are really going to be transformative. We go over subtle complications of quantum chemical research that tend to be overlooked in discussions involving quantum computers. We estimate quantum computer resources that will be required for performing calculations on quantum computers with chemical accuracy for several types of molecules. In particular, we directly compare the resources and timings associated with classical and quantum computers for the molecules H$_2$ for increasing basis set sizes, and Cr$_2$ for a variety of complete active spaces (CAS) within the scope of the CASCI and CASSCF methods. The results obtained for the chromium dimer enable us to estimate the size of the active space at which computations of non-dynamic correlation on a quantum computer should take less time than analogous computations on a classical computer. Using this result, we speculate on the types of chemical applications for which the use of quantum computers would be both beneficial and relevant to industrial applications in the short term.",https://arxiv.org/abs/2009.12472,"",,2024-02-07 00:08:11.373+00,2024-02-07 00:08:11.373+00,19,,2024-02-07 00:08:11.373+00,"","",139,Quantum computational chemistry (QCC),Quantum computational chemistry (QCC),the use of quantum computers to solve problems in computational quantum chemistry.,2022-07-14 12:21:27.924+00,2022-07-14 12:21:27.924+00,39,58,f,How will quantum computers provide an industrially relevant computational advantage in quantum chemistry?,752,Quantum computational chemistry (QCC)
+1631,t,Qubits,1366,2024-02-07 00:08:59.363+00,2024-02-07 00:08:59.363+00,,19,1029,1072,2020-09-25,N = 26,,,,,,,,1072,2024-02-07 00:08:42.595+00,2024-02-07 00:08:42.595+00,,19,752,139,752,How will quantum computers provide an industrially relevant computational advantage in quantum chemistry?,how will quantum computers provide an industrially relevant computational advantage in quantum chemistry?,"Numerous reports claim that quantum advantage, which should emerge as a direct consequence of the advent of quantum computers, will herald a new era of chemical research because it will enable scientists to perform the kinds of quantum chemical simulations that have not been possible before. Such simulations on quantum computers, promising a significantly greater accuracy and speed, are projected to exert a great impact on the way we can probe reality, predict the outcomes of chemical experiments, and even drive design of drugs, catalysts, and materials. In this work we review the current status of quantum hardware and algorithm theory and examine whether such popular claims about quantum advantage are really going to be transformative. We go over subtle complications of quantum chemical research that tend to be overlooked in discussions involving quantum computers. We estimate quantum computer resources that will be required for performing calculations on quantum computers with chemical accuracy for several types of molecules. In particular, we directly compare the resources and timings associated with classical and quantum computers for the molecules H$_2$ for increasing basis set sizes, and Cr$_2$ for a variety of complete active spaces (CAS) within the scope of the CASCI and CASSCF methods. The results obtained for the chromium dimer enable us to estimate the size of the active space at which computations of non-dynamic correlation on a quantum computer should take less time than analogous computations on a classical computer. Using this result, we speculate on the types of chemical applications for which the use of quantum computers would be both beneficial and relevant to industrial applications in the short term.",https://arxiv.org/abs/2009.12472,"",,2024-02-07 00:08:11.373+00,2024-02-07 00:08:11.373+00,19,,2024-02-07 00:08:11.373+00,"","",139,Quantum computational chemistry (QCC),Quantum computational chemistry (QCC),the use of quantum computers to solve problems in computational quantum chemistry.,2022-07-14 12:21:27.924+00,2022-07-14 12:21:27.924+00,39,58,f,How will quantum computers provide an industrially relevant computational advantage in quantum chemistry?,752,Quantum computational chemistry (QCC)
+1630,f,T-count,2.1e+17,2024-02-07 00:07:29.014+00,2024-02-07 00:07:29.014+00,,19,1028,1071,2023-08-23,PF,,,,,,,,1071,2024-02-07 00:04:46.292+00,2024-02-07 00:04:46.292+00,,19,751,209,751,Quantum computation of stopping power for inertial fusion target design,quantum computation of stopping power for inertial fusion target design,"Stopping power is the rate at which a material absorbs the kinetic energy of a charged particle passing through it -- one of many properties needed over a wide range of thermodynamic conditions in modeling inertial fusion implosions. First-principles stopping calculations are classically challenging because they involve the dynamics of large electronic systems far from equilibrium, with accuracies that are particularly difficult to constrain and assess in the warm-dense conditions preceding ignition. Here, we describe a protocol for using a fault-tolerant quantum computer to calculate stopping power from a first-quantized representation of the electrons and projectile. Our approach builds upon the electronic structure block encodings of Su et al. [PRX Quantum 2, 040332 2021], adapting and optimizing those algorithms to estimate observables of interest from the non-Born-Oppenheimer dynamics of multiple particle species at finite temperature. Ultimately, we report logical qubit requirements and leading-order Toffoli costs for computing the stopping power of various projectile/target combinations relevant to interpreting and designing inertial fusion experiments. We estimate that scientifically interesting and classically intractable stopping power calculations can be quantum simulated with roughly the same number of logical qubits and about one hundred times more Toffoli gates than is required for state-of-the-art quantum simulations of industrially relevant molecules such as FeMoCo or P450.",https://arxiv.org/abs/2308.12352,"",,2024-02-07 00:04:15.555+00,2024-02-07 00:04:15.555+00,19,,2024-02-07 00:04:15.555+00,"","",209,Materials,Materials,"",2024-02-06 23:22:03.723+00,2024-02-06 23:22:03.723+00,19,59,f,Quantum computation of stopping power for inertial fusion target design,751,Materials
+1629,t,Qubits,33368,2024-02-07 00:07:09.375+00,2024-02-07 00:07:09.375+00,,19,1028,1071,2023-08-23,PF,,,,,,,,1071,2024-02-07 00:04:46.292+00,2024-02-07 00:04:46.292+00,,19,751,209,751,Quantum computation of stopping power for inertial fusion target design,quantum computation of stopping power for inertial fusion target design,"Stopping power is the rate at which a material absorbs the kinetic energy of a charged particle passing through it -- one of many properties needed over a wide range of thermodynamic conditions in modeling inertial fusion implosions. First-principles stopping calculations are classically challenging because they involve the dynamics of large electronic systems far from equilibrium, with accuracies that are particularly difficult to constrain and assess in the warm-dense conditions preceding ignition. Here, we describe a protocol for using a fault-tolerant quantum computer to calculate stopping power from a first-quantized representation of the electrons and projectile. Our approach builds upon the electronic structure block encodings of Su et al. [PRX Quantum 2, 040332 2021], adapting and optimizing those algorithms to estimate observables of interest from the non-Born-Oppenheimer dynamics of multiple particle species at finite temperature. Ultimately, we report logical qubit requirements and leading-order Toffoli costs for computing the stopping power of various projectile/target combinations relevant to interpreting and designing inertial fusion experiments. We estimate that scientifically interesting and classically intractable stopping power calculations can be quantum simulated with roughly the same number of logical qubits and about one hundred times more Toffoli gates than is required for state-of-the-art quantum simulations of industrially relevant molecules such as FeMoCo or P450.",https://arxiv.org/abs/2308.12352,"",,2024-02-07 00:04:15.555+00,2024-02-07 00:04:15.555+00,19,,2024-02-07 00:04:15.555+00,"","",209,Materials,Materials,"",2024-02-06 23:22:03.723+00,2024-02-06 23:22:03.723+00,19,59,f,Quantum computation of stopping power for inertial fusion target design,751,Materials
+1628,f,T-count,2.1e+20,2024-02-07 00:06:49.379+00,2024-02-07 00:06:49.379+00,,19,1028,1071,2023-08-23,QSP,,,,,,,,1071,2024-02-07 00:04:46.292+00,2024-02-07 00:04:46.292+00,,19,751,209,751,Quantum computation of stopping power for inertial fusion target design,quantum computation of stopping power for inertial fusion target design,"Stopping power is the rate at which a material absorbs the kinetic energy of a charged particle passing through it -- one of many properties needed over a wide range of thermodynamic conditions in modeling inertial fusion implosions. First-principles stopping calculations are classically challenging because they involve the dynamics of large electronic systems far from equilibrium, with accuracies that are particularly difficult to constrain and assess in the warm-dense conditions preceding ignition. Here, we describe a protocol for using a fault-tolerant quantum computer to calculate stopping power from a first-quantized representation of the electrons and projectile. Our approach builds upon the electronic structure block encodings of Su et al. [PRX Quantum 2, 040332 2021], adapting and optimizing those algorithms to estimate observables of interest from the non-Born-Oppenheimer dynamics of multiple particle species at finite temperature. Ultimately, we report logical qubit requirements and leading-order Toffoli costs for computing the stopping power of various projectile/target combinations relevant to interpreting and designing inertial fusion experiments. We estimate that scientifically interesting and classically intractable stopping power calculations can be quantum simulated with roughly the same number of logical qubits and about one hundred times more Toffoli gates than is required for state-of-the-art quantum simulations of industrially relevant molecules such as FeMoCo or P450.",https://arxiv.org/abs/2308.12352,"",,2024-02-07 00:04:15.555+00,2024-02-07 00:04:15.555+00,19,,2024-02-07 00:04:15.555+00,"","",209,Materials,Materials,"",2024-02-06 23:22:03.723+00,2024-02-06 23:22:03.723+00,19,59,f,Quantum computation of stopping power for inertial fusion target design,751,Materials
+1627,t,Qubits,33038,2024-02-07 00:06:31.611+00,2024-02-07 00:06:31.611+00,,19,1028,1071,2023-08-23,QSP,,,,,,,,1071,2024-02-07 00:04:46.292+00,2024-02-07 00:04:46.292+00,,19,751,209,751,Quantum computation of stopping power for inertial fusion target design,quantum computation of stopping power for inertial fusion target design,"Stopping power is the rate at which a material absorbs the kinetic energy of a charged particle passing through it -- one of many properties needed over a wide range of thermodynamic conditions in modeling inertial fusion implosions. First-principles stopping calculations are classically challenging because they involve the dynamics of large electronic systems far from equilibrium, with accuracies that are particularly difficult to constrain and assess in the warm-dense conditions preceding ignition. Here, we describe a protocol for using a fault-tolerant quantum computer to calculate stopping power from a first-quantized representation of the electrons and projectile. Our approach builds upon the electronic structure block encodings of Su et al. [PRX Quantum 2, 040332 2021], adapting and optimizing those algorithms to estimate observables of interest from the non-Born-Oppenheimer dynamics of multiple particle species at finite temperature. Ultimately, we report logical qubit requirements and leading-order Toffoli costs for computing the stopping power of various projectile/target combinations relevant to interpreting and designing inertial fusion experiments. We estimate that scientifically interesting and classically intractable stopping power calculations can be quantum simulated with roughly the same number of logical qubits and about one hundred times more Toffoli gates than is required for state-of-the-art quantum simulations of industrially relevant molecules such as FeMoCo or P450.",https://arxiv.org/abs/2308.12352,"",,2024-02-07 00:04:15.555+00,2024-02-07 00:04:15.555+00,19,,2024-02-07 00:04:15.555+00,"","",209,Materials,Materials,"",2024-02-06 23:22:03.723+00,2024-02-06 23:22:03.723+00,19,59,f,Quantum computation of stopping power for inertial fusion target design,751,Materials
+1626,f,T-count,11000000000000,2024-02-07 00:06:11.862+00,2024-02-07 00:06:11.862+00,,19,1028,1071,2023-08-23,PF,,,,,,,,1071,2024-02-07 00:04:46.292+00,2024-02-07 00:04:46.292+00,,19,751,209,751,Quantum computation of stopping power for inertial fusion target design,quantum computation of stopping power for inertial fusion target design,"Stopping power is the rate at which a material absorbs the kinetic energy of a charged particle passing through it -- one of many properties needed over a wide range of thermodynamic conditions in modeling inertial fusion implosions. First-principles stopping calculations are classically challenging because they involve the dynamics of large electronic systems far from equilibrium, with accuracies that are particularly difficult to constrain and assess in the warm-dense conditions preceding ignition. Here, we describe a protocol for using a fault-tolerant quantum computer to calculate stopping power from a first-quantized representation of the electrons and projectile. Our approach builds upon the electronic structure block encodings of Su et al. [PRX Quantum 2, 040332 2021], adapting and optimizing those algorithms to estimate observables of interest from the non-Born-Oppenheimer dynamics of multiple particle species at finite temperature. Ultimately, we report logical qubit requirements and leading-order Toffoli costs for computing the stopping power of various projectile/target combinations relevant to interpreting and designing inertial fusion experiments. We estimate that scientifically interesting and classically intractable stopping power calculations can be quantum simulated with roughly the same number of logical qubits and about one hundred times more Toffoli gates than is required for state-of-the-art quantum simulations of industrially relevant molecules such as FeMoCo or P450.",https://arxiv.org/abs/2308.12352,"",,2024-02-07 00:04:15.555+00,2024-02-07 00:04:15.555+00,19,,2024-02-07 00:04:15.555+00,"","",209,Materials,Materials,"",2024-02-06 23:22:03.723+00,2024-02-06 23:22:03.723+00,19,59,f,Quantum computation of stopping power for inertial fusion target design,751,Materials
+1625,t,Qubits,2666,2024-02-07 00:05:51.25+00,2024-02-07 00:05:51.25+00,,19,1028,1071,2023-08-23,PF,,,,,,,,1071,2024-02-07 00:04:46.292+00,2024-02-07 00:04:46.292+00,,19,751,209,751,Quantum computation of stopping power for inertial fusion target design,quantum computation of stopping power for inertial fusion target design,"Stopping power is the rate at which a material absorbs the kinetic energy of a charged particle passing through it -- one of many properties needed over a wide range of thermodynamic conditions in modeling inertial fusion implosions. First-principles stopping calculations are classically challenging because they involve the dynamics of large electronic systems far from equilibrium, with accuracies that are particularly difficult to constrain and assess in the warm-dense conditions preceding ignition. Here, we describe a protocol for using a fault-tolerant quantum computer to calculate stopping power from a first-quantized representation of the electrons and projectile. Our approach builds upon the electronic structure block encodings of Su et al. [PRX Quantum 2, 040332 2021], adapting and optimizing those algorithms to estimate observables of interest from the non-Born-Oppenheimer dynamics of multiple particle species at finite temperature. Ultimately, we report logical qubit requirements and leading-order Toffoli costs for computing the stopping power of various projectile/target combinations relevant to interpreting and designing inertial fusion experiments. We estimate that scientifically interesting and classically intractable stopping power calculations can be quantum simulated with roughly the same number of logical qubits and about one hundred times more Toffoli gates than is required for state-of-the-art quantum simulations of industrially relevant molecules such as FeMoCo or P450.",https://arxiv.org/abs/2308.12352,"",,2024-02-07 00:04:15.555+00,2024-02-07 00:04:15.555+00,19,,2024-02-07 00:04:15.555+00,"","",209,Materials,Materials,"",2024-02-06 23:22:03.723+00,2024-02-06 23:22:03.723+00,19,59,f,Quantum computation of stopping power for inertial fusion target design,751,Materials
+1624,f,T-count,559999999999999.94,2024-02-07 00:05:32.056+00,2024-02-07 00:05:32.056+00,,19,1028,1071,2023-08-23,QSP,,,,,,,,1071,2024-02-07 00:04:46.292+00,2024-02-07 00:04:46.292+00,,19,751,209,751,Quantum computation of stopping power for inertial fusion target design,quantum computation of stopping power for inertial fusion target design,"Stopping power is the rate at which a material absorbs the kinetic energy of a charged particle passing through it -- one of many properties needed over a wide range of thermodynamic conditions in modeling inertial fusion implosions. First-principles stopping calculations are classically challenging because they involve the dynamics of large electronic systems far from equilibrium, with accuracies that are particularly difficult to constrain and assess in the warm-dense conditions preceding ignition. Here, we describe a protocol for using a fault-tolerant quantum computer to calculate stopping power from a first-quantized representation of the electrons and projectile. Our approach builds upon the electronic structure block encodings of Su et al. [PRX Quantum 2, 040332 2021], adapting and optimizing those algorithms to estimate observables of interest from the non-Born-Oppenheimer dynamics of multiple particle species at finite temperature. Ultimately, we report logical qubit requirements and leading-order Toffoli costs for computing the stopping power of various projectile/target combinations relevant to interpreting and designing inertial fusion experiments. We estimate that scientifically interesting and classically intractable stopping power calculations can be quantum simulated with roughly the same number of logical qubits and about one hundred times more Toffoli gates than is required for state-of-the-art quantum simulations of industrially relevant molecules such as FeMoCo or P450.",https://arxiv.org/abs/2308.12352,"",,2024-02-07 00:04:15.555+00,2024-02-07 00:04:15.555+00,19,,2024-02-07 00:04:15.555+00,"","",209,Materials,Materials,"",2024-02-06 23:22:03.723+00,2024-02-06 23:22:03.723+00,19,59,f,Quantum computation of stopping power for inertial fusion target design,751,Materials
+1623,t,Qubits,1749,2024-02-07 00:05:08.492+00,2024-02-07 00:05:08.492+00,,19,1028,1071,2023-08-23,QSP,,,,,,,,1071,2024-02-07 00:04:46.292+00,2024-02-07 00:04:46.292+00,,19,751,209,751,Quantum computation of stopping power for inertial fusion target design,quantum computation of stopping power for inertial fusion target design,"Stopping power is the rate at which a material absorbs the kinetic energy of a charged particle passing through it -- one of many properties needed over a wide range of thermodynamic conditions in modeling inertial fusion implosions. First-principles stopping calculations are classically challenging because they involve the dynamics of large electronic systems far from equilibrium, with accuracies that are particularly difficult to constrain and assess in the warm-dense conditions preceding ignition. Here, we describe a protocol for using a fault-tolerant quantum computer to calculate stopping power from a first-quantized representation of the electrons and projectile. Our approach builds upon the electronic structure block encodings of Su et al. [PRX Quantum 2, 040332 2021], adapting and optimizing those algorithms to estimate observables of interest from the non-Born-Oppenheimer dynamics of multiple particle species at finite temperature. Ultimately, we report logical qubit requirements and leading-order Toffoli costs for computing the stopping power of various projectile/target combinations relevant to interpreting and designing inertial fusion experiments. We estimate that scientifically interesting and classically intractable stopping power calculations can be quantum simulated with roughly the same number of logical qubits and about one hundred times more Toffoli gates than is required for state-of-the-art quantum simulations of industrially relevant molecules such as FeMoCo or P450.",https://arxiv.org/abs/2308.12352,"",,2024-02-07 00:04:15.555+00,2024-02-07 00:04:15.555+00,19,,2024-02-07 00:04:15.555+00,"","",209,Materials,Materials,"",2024-02-06 23:22:03.723+00,2024-02-06 23:22:03.723+00,19,59,f,Quantum computation of stopping power for inertial fusion target design,751,Materials
+1622,f,T-count,33900000000000,2024-02-06 23:58:07.984+00,2024-02-06 23:58:07.984+00,,19,1027,1070,2023-02-10,Sparse LCU,,,,,,,,1070,2024-02-06 23:54:34.09+00,2024-02-06 23:54:34.09+00,,19,750,209,750,Fault-tolerant quantum simulation of materials using Bloch orbitals,fault-tolerant quantum simulation of materials using bloch orbitals,"The simulation of chemistry is among the most promising applications of quantum computing. However, most prior work exploring algorithms for block-encoding, time-evolving, and sampling in the eigenbasis of electronic structure Hamiltonians has either focused on modeling finite-sized systems, or has required a large number of plane wave basis functions. In this work, we extend methods for quantum simulation with Bloch orbitals constructed from symmetry-adapted atom-centered orbitals so that one can model periodic \textit{ab initio} Hamiltonians using only a modest number of basis functions. We focus on adapting existing algorithms based on combining qubitization with tensor factorizations of the Coulomb operator. Significant modifications of those algorithms are required to obtain an asymptotic speedup leveraging translational (or, more broadly, Abelian) symmetries. We implement block encodings using known tensor factorizations and a new Bloch orbital form of tensor hypercontraction. Finally, we estimate the resources required to deploy our algorithms to classically challenging model materials relevant to the chemistry of Lithium Nickel Oxide battery cathodes within the surface code.",https://arxiv.org/abs/2302.05531,"",,2024-02-06 23:54:20.815+00,2024-02-06 23:54:20.815+00,19,,2024-02-06 23:54:20.815+00,"","",209,Materials,Materials,"",2024-02-06 23:22:03.723+00,2024-02-06 23:22:03.723+00,19,59,f,Fault-tolerant quantum simulation of materials using Bloch orbitals,750,Materials
+1621,t,Qubits,182864,2024-02-06 23:57:47.349+00,2024-02-06 23:57:47.349+00,,19,1027,1070,2023-02-10,Sparse LCU,,,,,,,,1070,2024-02-06 23:54:34.09+00,2024-02-06 23:54:34.09+00,,19,750,209,750,Fault-tolerant quantum simulation of materials using Bloch orbitals,fault-tolerant quantum simulation of materials using bloch orbitals,"The simulation of chemistry is among the most promising applications of quantum computing. However, most prior work exploring algorithms for block-encoding, time-evolving, and sampling in the eigenbasis of electronic structure Hamiltonians has either focused on modeling finite-sized systems, or has required a large number of plane wave basis functions. In this work, we extend methods for quantum simulation with Bloch orbitals constructed from symmetry-adapted atom-centered orbitals so that one can model periodic \textit{ab initio} Hamiltonians using only a modest number of basis functions. We focus on adapting existing algorithms based on combining qubitization with tensor factorizations of the Coulomb operator. Significant modifications of those algorithms are required to obtain an asymptotic speedup leveraging translational (or, more broadly, Abelian) symmetries. We implement block encodings using known tensor factorizations and a new Bloch orbital form of tensor hypercontraction. Finally, we estimate the resources required to deploy our algorithms to classically challenging model materials relevant to the chemistry of Lithium Nickel Oxide battery cathodes within the surface code.",https://arxiv.org/abs/2302.05531,"",,2024-02-06 23:54:20.815+00,2024-02-06 23:54:20.815+00,19,,2024-02-06 23:54:20.815+00,"","",209,Materials,Materials,"",2024-02-06 23:22:03.723+00,2024-02-06 23:22:03.723+00,19,59,f,Fault-tolerant quantum simulation of materials using Bloch orbitals,750,Materials
+1620,f,T-count,20600000000000,2024-02-06 23:57:28.101+00,2024-02-06 23:57:28.101+00,,19,1026,1070,2023-02-10,Sparse LCU,,,,,,,,1070,2024-02-06 23:54:34.09+00,2024-02-06 23:54:34.09+00,,19,750,209,750,Fault-tolerant quantum simulation of materials using Bloch orbitals,fault-tolerant quantum simulation of materials using bloch orbitals,"The simulation of chemistry is among the most promising applications of quantum computing. However, most prior work exploring algorithms for block-encoding, time-evolving, and sampling in the eigenbasis of electronic structure Hamiltonians has either focused on modeling finite-sized systems, or has required a large number of plane wave basis functions. In this work, we extend methods for quantum simulation with Bloch orbitals constructed from symmetry-adapted atom-centered orbitals so that one can model periodic \textit{ab initio} Hamiltonians using only a modest number of basis functions. We focus on adapting existing algorithms based on combining qubitization with tensor factorizations of the Coulomb operator. Significant modifications of those algorithms are required to obtain an asymptotic speedup leveraging translational (or, more broadly, Abelian) symmetries. We implement block encodings using known tensor factorizations and a new Bloch orbital form of tensor hypercontraction. Finally, we estimate the resources required to deploy our algorithms to classically challenging model materials relevant to the chemistry of Lithium Nickel Oxide battery cathodes within the surface code.",https://arxiv.org/abs/2302.05531,"",,2024-02-06 23:54:20.815+00,2024-02-06 23:54:20.815+00,19,,2024-02-06 23:54:20.815+00,"","",209,Materials,Materials,"",2024-02-06 23:22:03.723+00,2024-02-06 23:22:03.723+00,19,59,f,Fault-tolerant quantum simulation of materials using Bloch orbitals,750,Materials
+1619,t,Qubits,99918,2024-02-06 23:57:07.092+00,2024-02-06 23:57:07.092+00,,19,1026,1070,2023-02-10,Sparse LCU,,,,,,,,1070,2024-02-06 23:54:34.09+00,2024-02-06 23:54:34.09+00,,19,750,209,750,Fault-tolerant quantum simulation of materials using Bloch orbitals,fault-tolerant quantum simulation of materials using bloch orbitals,"The simulation of chemistry is among the most promising applications of quantum computing. However, most prior work exploring algorithms for block-encoding, time-evolving, and sampling in the eigenbasis of electronic structure Hamiltonians has either focused on modeling finite-sized systems, or has required a large number of plane wave basis functions. In this work, we extend methods for quantum simulation with Bloch orbitals constructed from symmetry-adapted atom-centered orbitals so that one can model periodic \textit{ab initio} Hamiltonians using only a modest number of basis functions. We focus on adapting existing algorithms based on combining qubitization with tensor factorizations of the Coulomb operator. Significant modifications of those algorithms are required to obtain an asymptotic speedup leveraging translational (or, more broadly, Abelian) symmetries. We implement block encodings using known tensor factorizations and a new Bloch orbital form of tensor hypercontraction. Finally, we estimate the resources required to deploy our algorithms to classically challenging model materials relevant to the chemistry of Lithium Nickel Oxide battery cathodes within the surface code.",https://arxiv.org/abs/2302.05531,"",,2024-02-06 23:54:20.815+00,2024-02-06 23:54:20.815+00,19,,2024-02-06 23:54:20.815+00,"","",209,Materials,Materials,"",2024-02-06 23:22:03.723+00,2024-02-06 23:22:03.723+00,19,59,f,Fault-tolerant quantum simulation of materials using Bloch orbitals,750,Materials
+1618,f,T-count,10300000000000,2024-02-06 23:56:49.662+00,2024-02-06 23:56:49.662+00,,19,1025,1070,2023-02-10,Sparse LCU,,,,,,,,1070,2024-02-06 23:54:34.09+00,2024-02-06 23:54:34.09+00,,19,750,209,750,Fault-tolerant quantum simulation of materials using Bloch orbitals,fault-tolerant quantum simulation of materials using bloch orbitals,"The simulation of chemistry is among the most promising applications of quantum computing. However, most prior work exploring algorithms for block-encoding, time-evolving, and sampling in the eigenbasis of electronic structure Hamiltonians has either focused on modeling finite-sized systems, or has required a large number of plane wave basis functions. In this work, we extend methods for quantum simulation with Bloch orbitals constructed from symmetry-adapted atom-centered orbitals so that one can model periodic \textit{ab initio} Hamiltonians using only a modest number of basis functions. We focus on adapting existing algorithms based on combining qubitization with tensor factorizations of the Coulomb operator. Significant modifications of those algorithms are required to obtain an asymptotic speedup leveraging translational (or, more broadly, Abelian) symmetries. We implement block encodings using known tensor factorizations and a new Bloch orbital form of tensor hypercontraction. Finally, we estimate the resources required to deploy our algorithms to classically challenging model materials relevant to the chemistry of Lithium Nickel Oxide battery cathodes within the surface code.",https://arxiv.org/abs/2302.05531,"",,2024-02-06 23:54:20.815+00,2024-02-06 23:54:20.815+00,19,,2024-02-06 23:54:20.815+00,"","",209,Materials,Materials,"",2024-02-06 23:22:03.723+00,2024-02-06 23:22:03.723+00,19,59,f,Fault-tolerant quantum simulation of materials using Bloch orbitals,750,Materials
+1617,t,Qubits,83532,2024-02-06 23:56:28.944+00,2024-02-06 23:56:28.944+00,,19,1025,1070,2023-02-10,Sparse LCU,,,,,,,,1070,2024-02-06 23:54:34.09+00,2024-02-06 23:54:34.09+00,,19,750,209,750,Fault-tolerant quantum simulation of materials using Bloch orbitals,fault-tolerant quantum simulation of materials using bloch orbitals,"The simulation of chemistry is among the most promising applications of quantum computing. However, most prior work exploring algorithms for block-encoding, time-evolving, and sampling in the eigenbasis of electronic structure Hamiltonians has either focused on modeling finite-sized systems, or has required a large number of plane wave basis functions. In this work, we extend methods for quantum simulation with Bloch orbitals constructed from symmetry-adapted atom-centered orbitals so that one can model periodic \textit{ab initio} Hamiltonians using only a modest number of basis functions. We focus on adapting existing algorithms based on combining qubitization with tensor factorizations of the Coulomb operator. Significant modifications of those algorithms are required to obtain an asymptotic speedup leveraging translational (or, more broadly, Abelian) symmetries. We implement block encodings using known tensor factorizations and a new Bloch orbital form of tensor hypercontraction. Finally, we estimate the resources required to deploy our algorithms to classically challenging model materials relevant to the chemistry of Lithium Nickel Oxide battery cathodes within the surface code.",https://arxiv.org/abs/2302.05531,"",,2024-02-06 23:54:20.815+00,2024-02-06 23:54:20.815+00,19,,2024-02-06 23:54:20.815+00,"","",209,Materials,Materials,"",2024-02-06 23:22:03.723+00,2024-02-06 23:22:03.723+00,19,59,f,Fault-tolerant quantum simulation of materials using Bloch orbitals,750,Materials
+1616,f,T-count,61600000000000,2024-02-06 23:56:07.742+00,2024-02-06 23:56:07.742+00,,19,1024,1070,2023-02-10,Sparse LCU,,,,,,,,1070,2024-02-06 23:54:34.09+00,2024-02-06 23:54:34.09+00,,19,750,209,750,Fault-tolerant quantum simulation of materials using Bloch orbitals,fault-tolerant quantum simulation of materials using bloch orbitals,"The simulation of chemistry is among the most promising applications of quantum computing. However, most prior work exploring algorithms for block-encoding, time-evolving, and sampling in the eigenbasis of electronic structure Hamiltonians has either focused on modeling finite-sized systems, or has required a large number of plane wave basis functions. In this work, we extend methods for quantum simulation with Bloch orbitals constructed from symmetry-adapted atom-centered orbitals so that one can model periodic \textit{ab initio} Hamiltonians using only a modest number of basis functions. We focus on adapting existing algorithms based on combining qubitization with tensor factorizations of the Coulomb operator. Significant modifications of those algorithms are required to obtain an asymptotic speedup leveraging translational (or, more broadly, Abelian) symmetries. We implement block encodings using known tensor factorizations and a new Bloch orbital form of tensor hypercontraction. Finally, we estimate the resources required to deploy our algorithms to classically challenging model materials relevant to the chemistry of Lithium Nickel Oxide battery cathodes within the surface code.",https://arxiv.org/abs/2302.05531,"",,2024-02-06 23:54:20.815+00,2024-02-06 23:54:20.815+00,19,,2024-02-06 23:54:20.815+00,"","",209,Materials,Materials,"",2024-02-06 23:22:03.723+00,2024-02-06 23:22:03.723+00,19,59,f,Fault-tolerant quantum simulation of materials using Bloch orbitals,750,Materials
+1615,t,Qubits,166946,2024-02-06 23:55:48.843+00,2024-02-06 23:55:48.843+00,,19,1024,1070,2023-02-10,Sparse LCU,,,,,,,,1070,2024-02-06 23:54:34.09+00,2024-02-06 23:54:34.09+00,,19,750,209,750,Fault-tolerant quantum simulation of materials using Bloch orbitals,fault-tolerant quantum simulation of materials using bloch orbitals,"The simulation of chemistry is among the most promising applications of quantum computing. However, most prior work exploring algorithms for block-encoding, time-evolving, and sampling in the eigenbasis of electronic structure Hamiltonians has either focused on modeling finite-sized systems, or has required a large number of plane wave basis functions. In this work, we extend methods for quantum simulation with Bloch orbitals constructed from symmetry-adapted atom-centered orbitals so that one can model periodic \textit{ab initio} Hamiltonians using only a modest number of basis functions. We focus on adapting existing algorithms based on combining qubitization with tensor factorizations of the Coulomb operator. Significant modifications of those algorithms are required to obtain an asymptotic speedup leveraging translational (or, more broadly, Abelian) symmetries. We implement block encodings using known tensor factorizations and a new Bloch orbital form of tensor hypercontraction. Finally, we estimate the resources required to deploy our algorithms to classically challenging model materials relevant to the chemistry of Lithium Nickel Oxide battery cathodes within the surface code.",https://arxiv.org/abs/2302.05531,"",,2024-02-06 23:54:20.815+00,2024-02-06 23:54:20.815+00,19,,2024-02-06 23:54:20.815+00,"","",209,Materials,Materials,"",2024-02-06 23:22:03.723+00,2024-02-06 23:22:03.723+00,19,59,f,Fault-tolerant quantum simulation of materials using Bloch orbitals,750,Materials
+1614,t,Qubits,1758,2024-02-06 23:47:13.65+00,2024-02-06 23:47:13.65+00,,19,1023,1069,2021-05-26,plane wave basis,,,,,,,,1069,2024-02-06 23:22:03.728+00,2024-02-06 23:22:03.728+00,,19,749,209,749,Fault-Tolerant Quantum Simulations of Chemistry in First Quantization,fault-tolerant quantum simulations of chemistry in first quantization,"Quantum simulations of chemistry in first quantization offer important advantages over approaches in second quantization including faster convergence to the continuum limit and the opportunity for practical simulations outside the Born-Oppenheimer approximation. However, as all prior work on quantum simulation in first quantization has been limited to asymptotic analysis, it has been impossible to compare the resources required for these approaches to those for more commonly studied algorithms in second quantization. Here, we analyze and optimize the resources required to implement two first quantized quantum algorithms for chemistry from Babbush et al that realize block encodings for the qubitization and interaction picture frameworks of Low et al. The two algorithms we study enable simulation with gate complexities $\tilde{\cal O}(η^{8/3}N^{1/3}t+η^{4/3}N^{2/3}t)$ and $\tilde{\cal O}(η^{8/3} N^{1/3} t)$ where $η$ is the number of electrons, $N$ is the number of plane wave basis functions, and $t$ is the duration of time-evolution ($t$ is inverse to target precision when the goal is to estimate energies). In addition to providing the first explicit circuits and constant factors for any first quantized simulation and introducing improvements which reduce circuit complexity by about a thousandfold over naive implementations for modest sized systems, we also describe new algorithms that asymptotically achieve the same scaling in a real space representation. We assess the resources required to simulate various molecules and materials and conclude that the qubitized algorithm will often be more practical than the interaction picture algorithm. We demonstrate that our qubitized algorithm often requires much less surface code spacetime volume for simulating millions of plane waves than the best second quantized algorithms require for simulating hundreds of Gaussian orbitals.",https://arxiv.org/abs/2105.12767,"",,2024-02-06 23:20:01.534+00,2024-02-06 23:20:01.534+00,19,,2024-02-06 23:20:01.533+00,"","",209,Materials,Materials,"",2024-02-06 23:22:03.723+00,2024-02-06 23:22:03.723+00,19,59,f,Fault-Tolerant Quantum Simulations of Chemistry in First Quantization,749,Materials
+1613,f,T-count,80000000000,2024-02-06 23:47:00.061+00,2024-02-06 23:47:00.061+00,,19,1023,1069,2021-05-26,plane wave basis,,,,,,,,1069,2024-02-06 23:22:03.728+00,2024-02-06 23:22:03.728+00,,19,749,209,749,Fault-Tolerant Quantum Simulations of Chemistry in First Quantization,fault-tolerant quantum simulations of chemistry in first quantization,"Quantum simulations of chemistry in first quantization offer important advantages over approaches in second quantization including faster convergence to the continuum limit and the opportunity for practical simulations outside the Born-Oppenheimer approximation. However, as all prior work on quantum simulation in first quantization has been limited to asymptotic analysis, it has been impossible to compare the resources required for these approaches to those for more commonly studied algorithms in second quantization. Here, we analyze and optimize the resources required to implement two first quantized quantum algorithms for chemistry from Babbush et al that realize block encodings for the qubitization and interaction picture frameworks of Low et al. The two algorithms we study enable simulation with gate complexities $\tilde{\cal O}(η^{8/3}N^{1/3}t+η^{4/3}N^{2/3}t)$ and $\tilde{\cal O}(η^{8/3} N^{1/3} t)$ where $η$ is the number of electrons, $N$ is the number of plane wave basis functions, and $t$ is the duration of time-evolution ($t$ is inverse to target precision when the goal is to estimate energies). In addition to providing the first explicit circuits and constant factors for any first quantized simulation and introducing improvements which reduce circuit complexity by about a thousandfold over naive implementations for modest sized systems, we also describe new algorithms that asymptotically achieve the same scaling in a real space representation. We assess the resources required to simulate various molecules and materials and conclude that the qubitized algorithm will often be more practical than the interaction picture algorithm. We demonstrate that our qubitized algorithm often requires much less surface code spacetime volume for simulating millions of plane waves than the best second quantized algorithms require for simulating hundreds of Gaussian orbitals.",https://arxiv.org/abs/2105.12767,"",,2024-02-06 23:20:01.534+00,2024-02-06 23:20:01.534+00,19,,2024-02-06 23:20:01.533+00,"","",209,Materials,Materials,"",2024-02-06 23:22:03.723+00,2024-02-06 23:22:03.723+00,19,59,f,Fault-Tolerant Quantum Simulations of Chemistry in First Quantization,749,Materials
+1612,f,T-count,25000000000,2024-02-06 23:46:32.005+00,2024-02-06 23:46:32.005+00,,19,1022,1069,2021-05-26,plane wave basis,,,,,,,,1069,2024-02-06 23:22:03.728+00,2024-02-06 23:22:03.728+00,,19,749,209,749,Fault-Tolerant Quantum Simulations of Chemistry in First Quantization,fault-tolerant quantum simulations of chemistry in first quantization,"Quantum simulations of chemistry in first quantization offer important advantages over approaches in second quantization including faster convergence to the continuum limit and the opportunity for practical simulations outside the Born-Oppenheimer approximation. However, as all prior work on quantum simulation in first quantization has been limited to asymptotic analysis, it has been impossible to compare the resources required for these approaches to those for more commonly studied algorithms in second quantization. Here, we analyze and optimize the resources required to implement two first quantized quantum algorithms for chemistry from Babbush et al that realize block encodings for the qubitization and interaction picture frameworks of Low et al. The two algorithms we study enable simulation with gate complexities $\tilde{\cal O}(η^{8/3}N^{1/3}t+η^{4/3}N^{2/3}t)$ and $\tilde{\cal O}(η^{8/3} N^{1/3} t)$ where $η$ is the number of electrons, $N$ is the number of plane wave basis functions, and $t$ is the duration of time-evolution ($t$ is inverse to target precision when the goal is to estimate energies). In addition to providing the first explicit circuits and constant factors for any first quantized simulation and introducing improvements which reduce circuit complexity by about a thousandfold over naive implementations for modest sized systems, we also describe new algorithms that asymptotically achieve the same scaling in a real space representation. We assess the resources required to simulate various molecules and materials and conclude that the qubitized algorithm will often be more practical than the interaction picture algorithm. We demonstrate that our qubitized algorithm often requires much less surface code spacetime volume for simulating millions of plane waves than the best second quantized algorithms require for simulating hundreds of Gaussian orbitals.",https://arxiv.org/abs/2105.12767,"",,2024-02-06 23:20:01.534+00,2024-02-06 23:20:01.534+00,19,,2024-02-06 23:20:01.533+00,"","",209,Materials,Materials,"",2024-02-06 23:22:03.723+00,2024-02-06 23:22:03.723+00,19,59,f,Fault-Tolerant Quantum Simulations of Chemistry in First Quantization,749,Materials
+1611,t,Qubits,1395,2024-02-06 23:46:05.072+00,2024-02-06 23:46:05.072+00,,19,1022,1069,2021-05-26,plane wave basis,,,,,,,,1069,2024-02-06 23:22:03.728+00,2024-02-06 23:22:03.728+00,,19,749,209,749,Fault-Tolerant Quantum Simulations of Chemistry in First Quantization,fault-tolerant quantum simulations of chemistry in first quantization,"Quantum simulations of chemistry in first quantization offer important advantages over approaches in second quantization including faster convergence to the continuum limit and the opportunity for practical simulations outside the Born-Oppenheimer approximation. However, as all prior work on quantum simulation in first quantization has been limited to asymptotic analysis, it has been impossible to compare the resources required for these approaches to those for more commonly studied algorithms in second quantization. Here, we analyze and optimize the resources required to implement two first quantized quantum algorithms for chemistry from Babbush et al that realize block encodings for the qubitization and interaction picture frameworks of Low et al. The two algorithms we study enable simulation with gate complexities $\tilde{\cal O}(η^{8/3}N^{1/3}t+η^{4/3}N^{2/3}t)$ and $\tilde{\cal O}(η^{8/3} N^{1/3} t)$ where $η$ is the number of electrons, $N$ is the number of plane wave basis functions, and $t$ is the duration of time-evolution ($t$ is inverse to target precision when the goal is to estimate energies). In addition to providing the first explicit circuits and constant factors for any first quantized simulation and introducing improvements which reduce circuit complexity by about a thousandfold over naive implementations for modest sized systems, we also describe new algorithms that asymptotically achieve the same scaling in a real space representation. We assess the resources required to simulate various molecules and materials and conclude that the qubitized algorithm will often be more practical than the interaction picture algorithm. We demonstrate that our qubitized algorithm often requires much less surface code spacetime volume for simulating millions of plane waves than the best second quantized algorithms require for simulating hundreds of Gaussian orbitals.",https://arxiv.org/abs/2105.12767,"",,2024-02-06 23:20:01.534+00,2024-02-06 23:20:01.534+00,19,,2024-02-06 23:20:01.533+00,"","",209,Materials,Materials,"",2024-02-06 23:22:03.723+00,2024-02-06 23:22:03.723+00,19,59,f,Fault-Tolerant Quantum Simulations of Chemistry in First Quantization,749,Materials
+1609,f,T-count,63200000000,2024-02-06 23:19:01.173+00,2024-02-06 23:19:01.173+00,,19,1020,1068,2021-04-21,STO-3G basis,,,,,,,,1068,2024-02-06 23:16:59.664+00,2024-02-06 23:16:59.664+00,,19,748,139,748,Fault-tolerant resource estimate for quantum chemical simulations: Case study on Li-ion battery electrolyte molecules,fault-tolerant resource estimate for quantum chemical simulations: case study on li-ion battery electrolyte molecules,"We estimate the resources required in the fusion-based quantum computing scheme to simulate electrolyte molecules in Li-ion batteries on a fault-tolerant, photonic quantum computer. We focus on the molecules that can provide practical solutions to industrially relevant problems. Certain fault-tolerant operations require the use of single-qubit ""magic states"" prepared by dedicated ""magic state factories"" (MSFs). Producing and consuming magic states in parallel is typically a prohibitively expensive task, resulting in the serial application of fault-tolerant gates. However, for the systems considered, the MSF constitutes a negligible fraction of the total footprint of the quantum computer, allowing for the use of multiple MSFs to produce magic states in parallel. We suggest architectural and algorithmic techniques that can accommodate such a capability. We propose a method to consume multiple magic states simultaneously, which can potentially lead to an order of magnitude reduction in the computational runtime without additional expense in the footprint.",https://arxiv.org/abs/2104.10653,"",,2024-02-06 23:15:09.095+00,2024-02-06 23:15:09.095+00,19,,2024-02-06 23:15:09.094+00,"","",139,Quantum computational chemistry (QCC),Quantum computational chemistry (QCC),the use of quantum computers to solve problems in computational quantum chemistry.,2022-07-14 12:21:27.924+00,2022-07-14 12:21:27.924+00,39,58,f,Fault-tolerant resource estimate for quantum chemical simulations: Case study on Li-ion battery electrolyte molecules,748,Quantum computational chemistry (QCC)
+1608,f,T-count,541000000000,2024-02-06 23:18:32.509+00,2024-02-06 23:18:32.509+00,,19,1020,1068,2021-04-21,DZ basis,,,,,,,,1068,2024-02-06 23:16:59.664+00,2024-02-06 23:16:59.664+00,,19,748,139,748,Fault-tolerant resource estimate for quantum chemical simulations: Case study on Li-ion battery electrolyte molecules,fault-tolerant resource estimate for quantum chemical simulations: case study on li-ion battery electrolyte molecules,"We estimate the resources required in the fusion-based quantum computing scheme to simulate electrolyte molecules in Li-ion batteries on a fault-tolerant, photonic quantum computer. We focus on the molecules that can provide practical solutions to industrially relevant problems. Certain fault-tolerant operations require the use of single-qubit ""magic states"" prepared by dedicated ""magic state factories"" (MSFs). Producing and consuming magic states in parallel is typically a prohibitively expensive task, resulting in the serial application of fault-tolerant gates. However, for the systems considered, the MSF constitutes a negligible fraction of the total footprint of the quantum computer, allowing for the use of multiple MSFs to produce magic states in parallel. We suggest architectural and algorithmic techniques that can accommodate such a capability. We propose a method to consume multiple magic states simultaneously, which can potentially lead to an order of magnitude reduction in the computational runtime without additional expense in the footprint.",https://arxiv.org/abs/2104.10653,"",,2024-02-06 23:15:09.095+00,2024-02-06 23:15:09.095+00,19,,2024-02-06 23:15:09.094+00,"","",139,Quantum computational chemistry (QCC),Quantum computational chemistry (QCC),the use of quantum computers to solve problems in computational quantum chemistry.,2022-07-14 12:21:27.924+00,2022-07-14 12:21:27.924+00,39,58,f,Fault-tolerant resource estimate for quantum chemical simulations: Case study on Li-ion battery electrolyte molecules,748,Quantum computational chemistry (QCC)
+1607,f,T-count,62500000000000,2024-02-06 23:18:14.385+00,2024-02-06 23:18:14.385+00,,19,1020,1068,2021-04-21,cc-p VTZ basis,,,,,,,,1068,2024-02-06 23:16:59.664+00,2024-02-06 23:16:59.664+00,,19,748,139,748,Fault-tolerant resource estimate for quantum chemical simulations: Case study on Li-ion battery electrolyte molecules,fault-tolerant resource estimate for quantum chemical simulations: case study on li-ion battery electrolyte molecules,"We estimate the resources required in the fusion-based quantum computing scheme to simulate electrolyte molecules in Li-ion batteries on a fault-tolerant, photonic quantum computer. We focus on the molecules that can provide practical solutions to industrially relevant problems. Certain fault-tolerant operations require the use of single-qubit ""magic states"" prepared by dedicated ""magic state factories"" (MSFs). Producing and consuming magic states in parallel is typically a prohibitively expensive task, resulting in the serial application of fault-tolerant gates. However, for the systems considered, the MSF constitutes a negligible fraction of the total footprint of the quantum computer, allowing for the use of multiple MSFs to produce magic states in parallel. We suggest architectural and algorithmic techniques that can accommodate such a capability. We propose a method to consume multiple magic states simultaneously, which can potentially lead to an order of magnitude reduction in the computational runtime without additional expense in the footprint.",https://arxiv.org/abs/2104.10653,"",,2024-02-06 23:15:09.095+00,2024-02-06 23:15:09.095+00,19,,2024-02-06 23:15:09.094+00,"","",139,Quantum computational chemistry (QCC),Quantum computational chemistry (QCC),the use of quantum computers to solve problems in computational quantum chemistry.,2022-07-14 12:21:27.924+00,2022-07-14 12:21:27.924+00,39,58,f,Fault-tolerant resource estimate for quantum chemical simulations: Case study on Li-ion battery electrolyte molecules,748,Quantum computational chemistry (QCC)
+1606,t,Qubits,81958,2024-02-06 23:17:56.234+00,2024-02-06 23:17:56.234+00,,19,1020,1068,2021-04-21,cc-p VTZ basis,,,,,,,,1068,2024-02-06 23:16:59.664+00,2024-02-06 23:16:59.664+00,,19,748,139,748,Fault-tolerant resource estimate for quantum chemical simulations: Case study on Li-ion battery electrolyte molecules,fault-tolerant resource estimate for quantum chemical simulations: case study on li-ion battery electrolyte molecules,"We estimate the resources required in the fusion-based quantum computing scheme to simulate electrolyte molecules in Li-ion batteries on a fault-tolerant, photonic quantum computer. We focus on the molecules that can provide practical solutions to industrially relevant problems. Certain fault-tolerant operations require the use of single-qubit ""magic states"" prepared by dedicated ""magic state factories"" (MSFs). Producing and consuming magic states in parallel is typically a prohibitively expensive task, resulting in the serial application of fault-tolerant gates. However, for the systems considered, the MSF constitutes a negligible fraction of the total footprint of the quantum computer, allowing for the use of multiple MSFs to produce magic states in parallel. We suggest architectural and algorithmic techniques that can accommodate such a capability. We propose a method to consume multiple magic states simultaneously, which can potentially lead to an order of magnitude reduction in the computational runtime without additional expense in the footprint.",https://arxiv.org/abs/2104.10653,"",,2024-02-06 23:15:09.095+00,2024-02-06 23:15:09.095+00,19,,2024-02-06 23:15:09.094+00,"","",139,Quantum computational chemistry (QCC),Quantum computational chemistry (QCC),the use of quantum computers to solve problems in computational quantum chemistry.,2022-07-14 12:21:27.924+00,2022-07-14 12:21:27.924+00,39,58,f,Fault-tolerant resource estimate for quantum chemical simulations: Case study on Li-ion battery electrolyte molecules,748,Quantum computational chemistry (QCC)
+1605,t,Qubits,10462,2024-02-06 23:17:37.041+00,2024-02-06 23:17:37.041+00,,19,1020,1068,2021-04-21,DZ basis,,,,,,,,1068,2024-02-06 23:16:59.664+00,2024-02-06 23:16:59.664+00,,19,748,139,748,Fault-tolerant resource estimate for quantum chemical simulations: Case study on Li-ion battery electrolyte molecules,fault-tolerant resource estimate for quantum chemical simulations: case study on li-ion battery electrolyte molecules,"We estimate the resources required in the fusion-based quantum computing scheme to simulate electrolyte molecules in Li-ion batteries on a fault-tolerant, photonic quantum computer. We focus on the molecules that can provide practical solutions to industrially relevant problems. Certain fault-tolerant operations require the use of single-qubit ""magic states"" prepared by dedicated ""magic state factories"" (MSFs). Producing and consuming magic states in parallel is typically a prohibitively expensive task, resulting in the serial application of fault-tolerant gates. However, for the systems considered, the MSF constitutes a negligible fraction of the total footprint of the quantum computer, allowing for the use of multiple MSFs to produce magic states in parallel. We suggest architectural and algorithmic techniques that can accommodate such a capability. We propose a method to consume multiple magic states simultaneously, which can potentially lead to an order of magnitude reduction in the computational runtime without additional expense in the footprint.",https://arxiv.org/abs/2104.10653,"",,2024-02-06 23:15:09.095+00,2024-02-06 23:15:09.095+00,19,,2024-02-06 23:15:09.094+00,"","",139,Quantum computational chemistry (QCC),Quantum computational chemistry (QCC),the use of quantum computers to solve problems in computational quantum chemistry.,2022-07-14 12:21:27.924+00,2022-07-14 12:21:27.924+00,39,58,f,Fault-tolerant resource estimate for quantum chemical simulations: Case study on Li-ion battery electrolyte molecules,748,Quantum computational chemistry (QCC)
+1604,t,Qubits,2685,2024-02-06 23:17:20.521+00,2024-02-06 23:17:20.521+00,,19,1020,1068,2021-04-21,STO-3G basis,,,,,,,,1068,2024-02-06 23:16:59.664+00,2024-02-06 23:16:59.664+00,,19,748,139,748,Fault-tolerant resource estimate for quantum chemical simulations: Case study on Li-ion battery electrolyte molecules,fault-tolerant resource estimate for quantum chemical simulations: case study on li-ion battery electrolyte molecules,"We estimate the resources required in the fusion-based quantum computing scheme to simulate electrolyte molecules in Li-ion batteries on a fault-tolerant, photonic quantum computer. We focus on the molecules that can provide practical solutions to industrially relevant problems. Certain fault-tolerant operations require the use of single-qubit ""magic states"" prepared by dedicated ""magic state factories"" (MSFs). Producing and consuming magic states in parallel is typically a prohibitively expensive task, resulting in the serial application of fault-tolerant gates. However, for the systems considered, the MSF constitutes a negligible fraction of the total footprint of the quantum computer, allowing for the use of multiple MSFs to produce magic states in parallel. We suggest architectural and algorithmic techniques that can accommodate such a capability. We propose a method to consume multiple magic states simultaneously, which can potentially lead to an order of magnitude reduction in the computational runtime without additional expense in the footprint.",https://arxiv.org/abs/2104.10653,"",,2024-02-06 23:15:09.095+00,2024-02-06 23:15:09.095+00,19,,2024-02-06 23:15:09.094+00,"","",139,Quantum computational chemistry (QCC),Quantum computational chemistry (QCC),the use of quantum computers to solve problems in computational quantum chemistry.,2022-07-14 12:21:27.924+00,2022-07-14 12:21:27.924+00,39,58,f,Fault-tolerant resource estimate for quantum chemical simulations: Case study on Li-ion battery electrolyte molecules,748,Quantum computational chemistry (QCC)
+1603,f,T-count,7800000000,2024-02-06 23:14:17.176+00,2024-02-06 23:14:17.176+00,,19,977,1038,2022-02-02,M = 320,,,,,,,,1038,2023-10-30 13:46:09.753+00,2023-10-30 13:46:09.753+00,,9,726,139,726,Reliably assessing the electronic structure of cytochrome P450 on today's classical computers and tomorrow's quantum computers,reliably assessing the electronic structure of cytochrome p450 on today's classical computers and tomorrow's quantum computers,"An accurate assessment of how quantum computers can be used for chemical simulation, especially their potential computational advantages, provides important context on how to deploy these future devices. In order to perform this assessment reliably, quantum resource estimates must be coupled with classical simulations attempting to answer relevant chemical questions and to define the classical simulation frontier. Herein, we explore the quantum and classical resources required to assess the electronic structure of cytochrome P450 enzymes (CYPs) and thus define a classical-quantum advantage boundary. This is accomplished by analyzing the convergence of DMRG+NEVPT2 and coupled cluster singles doubles with non-iterative triples (CCSD(T)) calculations for spin-gaps in models of the CYP catalytic cycle that indicate multireference character. The quantum resources required to perform phase estimation using qubitized quantum walks are calculated for the same systems. Compilation into the surface-code provides runtime estimates to compare directly to DMRG runtimes and to evaluate potential quantum advantage. Both classical and quantum resource estimates suggest that simulation of CYP models at scales large enough to balance dynamic and multiconfigurational electron correlation has the potential to be a quantum advantage problem and emphasizes the important interplay between classical simulations and quantum algorithms development for chemical simulation.",https://arxiv.org/abs/2202.01244,"",2023-11-30 17:31:32.247+00,2023-10-30 13:46:09.741+00,2023-11-30 17:31:32.247+00,9,,2023-10-30 13:46:09.74+00,"","",139,Quantum computational chemistry (QCC),Quantum computational chemistry (QCC),the use of quantum computers to solve problems in computational quantum chemistry.,2022-07-14 12:21:27.924+00,2022-07-14 12:21:27.924+00,39,58,f,Reliably assessing the electronic structure of cytochrome P450 on today's classical computers and tomorrow's quantum computers,726,Quantum computational chemistry (QCC)
+1602,t,Qubits,1434,2024-02-06 23:13:57.146+00,2024-02-06 23:13:57.146+00,,19,977,1038,2022-02-02,M = 320,,,,,,,,1038,2023-10-30 13:46:09.753+00,2023-10-30 13:46:09.753+00,,9,726,139,726,Reliably assessing the electronic structure of cytochrome P450 on today's classical computers and tomorrow's quantum computers,reliably assessing the electronic structure of cytochrome p450 on today's classical computers and tomorrow's quantum computers,"An accurate assessment of how quantum computers can be used for chemical simulation, especially their potential computational advantages, provides important context on how to deploy these future devices. In order to perform this assessment reliably, quantum resource estimates must be coupled with classical simulations attempting to answer relevant chemical questions and to define the classical simulation frontier. Herein, we explore the quantum and classical resources required to assess the electronic structure of cytochrome P450 enzymes (CYPs) and thus define a classical-quantum advantage boundary. This is accomplished by analyzing the convergence of DMRG+NEVPT2 and coupled cluster singles doubles with non-iterative triples (CCSD(T)) calculations for spin-gaps in models of the CYP catalytic cycle that indicate multireference character. The quantum resources required to perform phase estimation using qubitized quantum walks are calculated for the same systems. Compilation into the surface-code provides runtime estimates to compare directly to DMRG runtimes and to evaluate potential quantum advantage. Both classical and quantum resource estimates suggest that simulation of CYP models at scales large enough to balance dynamic and multiconfigurational electron correlation has the potential to be a quantum advantage problem and emphasizes the important interplay between classical simulations and quantum algorithms development for chemical simulation.",https://arxiv.org/abs/2202.01244,"",2023-11-30 17:31:32.247+00,2023-10-30 13:46:09.741+00,2023-11-30 17:31:32.247+00,9,,2023-10-30 13:46:09.74+00,"","",139,Quantum computational chemistry (QCC),Quantum computational chemistry (QCC),the use of quantum computers to solve problems in computational quantum chemistry.,2022-07-14 12:21:27.924+00,2022-07-14 12:21:27.924+00,39,58,f,Reliably assessing the electronic structure of cytochrome P450 on today's classical computers and tomorrow's quantum computers,726,Quantum computational chemistry (QCC)
+1601,f,T-count,32000000000,2024-02-06 23:12:26.233+00,2024-02-06 23:12:26.233+00,,19,1019,1067,2020-11-06,LLDUC orbitals,,,,,,,,1067,2024-02-06 23:10:31.79+00,2024-02-06 23:10:31.79+00,,19,747,208,747,Even more efficient quantum computations of chemistry through tensor hypercontraction,even more efficient quantum computations of chemistry through tensor hypercontraction,"We describe quantum circuits with only $\widetilde{\cal O}(N)$ Toffoli complexity that block encode the spectra of quantum chemistry Hamiltonians in a basis of $N$ arbitrary (e.g., molecular) orbitals. With ${\cal O}(λ/ ε)$ repetitions of these circuits one can use phase estimation to sample in the molecular eigenbasis, where $λ$ is the 1-norm of Hamiltonian coefficients and $ε$ is the target precision. This is the lowest complexity that has been shown for quantum computations of chemistry within an arbitrary basis. Furthermore, up to logarithmic factors, this matches the scaling of the most efficient prior block encodings that can only work with orthogonal basis functions diagonalizing the Coloumb operator (e.g., the plane wave dual basis). Our key insight is to factorize the Hamiltonian using a method known as tensor hypercontraction (THC) and then to transform the Coulomb operator into an isospectral diagonal form with a non-orthogonal basis defined by the THC factors. We then use qubitization to simulate the non-orthogonal THC Hamiltonian, in a fashion that avoids most complications of the non-orthogonal basis. We also reanalyze and reduce the cost of several of the best prior algorithms for these simulations in order to facilitate a clear comparison to the present work. In addition to having lower asymptotic scaling spacetime volume, compilation of our algorithm for challenging finite-sized molecules such as FeMoCo reveals that our method requires the least fault-tolerant resources of any known approach. By laying out and optimizing the surface code resources required of our approach we show that FeMoCo can be simulated using about four million physical qubits and under four days of runtime, assuming $1\,μ$s cycle times and physical gate error rates no worse than $0.1\%$.",https://arxiv.org/abs/2011.03494,"",,2024-02-06 23:10:13.832+00,2024-02-06 23:10:13.832+00,19,,2024-02-06 23:10:13.832+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Even more efficient quantum computations of chemistry through tensor hypercontraction,747,Physical simulation
+1600,t,Qubits,2196,2024-02-06 23:11:37.648+00,2024-02-06 23:11:37.648+00,,19,1019,1067,2020-11-06,LLDUC orbitals,,,,,,,,1067,2024-02-06 23:10:31.79+00,2024-02-06 23:10:31.79+00,,19,747,208,747,Even more efficient quantum computations of chemistry through tensor hypercontraction,even more efficient quantum computations of chemistry through tensor hypercontraction,"We describe quantum circuits with only $\widetilde{\cal O}(N)$ Toffoli complexity that block encode the spectra of quantum chemistry Hamiltonians in a basis of $N$ arbitrary (e.g., molecular) orbitals. With ${\cal O}(λ/ ε)$ repetitions of these circuits one can use phase estimation to sample in the molecular eigenbasis, where $λ$ is the 1-norm of Hamiltonian coefficients and $ε$ is the target precision. This is the lowest complexity that has been shown for quantum computations of chemistry within an arbitrary basis. Furthermore, up to logarithmic factors, this matches the scaling of the most efficient prior block encodings that can only work with orthogonal basis functions diagonalizing the Coloumb operator (e.g., the plane wave dual basis). Our key insight is to factorize the Hamiltonian using a method known as tensor hypercontraction (THC) and then to transform the Coulomb operator into an isospectral diagonal form with a non-orthogonal basis defined by the THC factors. We then use qubitization to simulate the non-orthogonal THC Hamiltonian, in a fashion that avoids most complications of the non-orthogonal basis. We also reanalyze and reduce the cost of several of the best prior algorithms for these simulations in order to facilitate a clear comparison to the present work. In addition to having lower asymptotic scaling spacetime volume, compilation of our algorithm for challenging finite-sized molecules such as FeMoCo reveals that our method requires the least fault-tolerant resources of any known approach. By laying out and optimizing the surface code resources required of our approach we show that FeMoCo can be simulated using about four million physical qubits and under four days of runtime, assuming $1\,μ$s cycle times and physical gate error rates no worse than $0.1\%$.",https://arxiv.org/abs/2011.03494,"",,2024-02-06 23:10:13.832+00,2024-02-06 23:10:13.832+00,19,,2024-02-06 23:10:13.832+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Even more efficient quantum computations of chemistry through tensor hypercontraction,747,Physical simulation
+1599,f,T-count,5300000000,2024-02-06 23:11:14.225+00,2024-02-06 23:11:14.225+00,,19,1019,1067,2020-11-06,RWSWT orbitals,,,,,,,,1067,2024-02-06 23:10:31.79+00,2024-02-06 23:10:31.79+00,,19,747,208,747,Even more efficient quantum computations of chemistry through tensor hypercontraction,even more efficient quantum computations of chemistry through tensor hypercontraction,"We describe quantum circuits with only $\widetilde{\cal O}(N)$ Toffoli complexity that block encode the spectra of quantum chemistry Hamiltonians in a basis of $N$ arbitrary (e.g., molecular) orbitals. With ${\cal O}(λ/ ε)$ repetitions of these circuits one can use phase estimation to sample in the molecular eigenbasis, where $λ$ is the 1-norm of Hamiltonian coefficients and $ε$ is the target precision. This is the lowest complexity that has been shown for quantum computations of chemistry within an arbitrary basis. Furthermore, up to logarithmic factors, this matches the scaling of the most efficient prior block encodings that can only work with orthogonal basis functions diagonalizing the Coloumb operator (e.g., the plane wave dual basis). Our key insight is to factorize the Hamiltonian using a method known as tensor hypercontraction (THC) and then to transform the Coulomb operator into an isospectral diagonal form with a non-orthogonal basis defined by the THC factors. We then use qubitization to simulate the non-orthogonal THC Hamiltonian, in a fashion that avoids most complications of the non-orthogonal basis. We also reanalyze and reduce the cost of several of the best prior algorithms for these simulations in order to facilitate a clear comparison to the present work. In addition to having lower asymptotic scaling spacetime volume, compilation of our algorithm for challenging finite-sized molecules such as FeMoCo reveals that our method requires the least fault-tolerant resources of any known approach. By laying out and optimizing the surface code resources required of our approach we show that FeMoCo can be simulated using about four million physical qubits and under four days of runtime, assuming $1\,μ$s cycle times and physical gate error rates no worse than $0.1\%$.",https://arxiv.org/abs/2011.03494,"",,2024-02-06 23:10:13.832+00,2024-02-06 23:10:13.832+00,19,,2024-02-06 23:10:13.832+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Even more efficient quantum computations of chemistry through tensor hypercontraction,747,Physical simulation
+1598,t,Qubits,2142,2024-02-06 23:10:51.479+00,2024-02-06 23:10:51.479+00,,19,1019,1067,2020-11-06,RWSWT orbitals,,,,,,,,1067,2024-02-06 23:10:31.79+00,2024-02-06 23:10:31.79+00,,19,747,208,747,Even more efficient quantum computations of chemistry through tensor hypercontraction,even more efficient quantum computations of chemistry through tensor hypercontraction,"We describe quantum circuits with only $\widetilde{\cal O}(N)$ Toffoli complexity that block encode the spectra of quantum chemistry Hamiltonians in a basis of $N$ arbitrary (e.g., molecular) orbitals. With ${\cal O}(λ/ ε)$ repetitions of these circuits one can use phase estimation to sample in the molecular eigenbasis, where $λ$ is the 1-norm of Hamiltonian coefficients and $ε$ is the target precision. This is the lowest complexity that has been shown for quantum computations of chemistry within an arbitrary basis. Furthermore, up to logarithmic factors, this matches the scaling of the most efficient prior block encodings that can only work with orthogonal basis functions diagonalizing the Coloumb operator (e.g., the plane wave dual basis). Our key insight is to factorize the Hamiltonian using a method known as tensor hypercontraction (THC) and then to transform the Coulomb operator into an isospectral diagonal form with a non-orthogonal basis defined by the THC factors. We then use qubitization to simulate the non-orthogonal THC Hamiltonian, in a fashion that avoids most complications of the non-orthogonal basis. We also reanalyze and reduce the cost of several of the best prior algorithms for these simulations in order to facilitate a clear comparison to the present work. In addition to having lower asymptotic scaling spacetime volume, compilation of our algorithm for challenging finite-sized molecules such as FeMoCo reveals that our method requires the least fault-tolerant resources of any known approach. By laying out and optimizing the surface code resources required of our approach we show that FeMoCo can be simulated using about four million physical qubits and under four days of runtime, assuming $1\,μ$s cycle times and physical gate error rates no worse than $0.1\%$.",https://arxiv.org/abs/2011.03494,"",,2024-02-06 23:10:13.832+00,2024-02-06 23:10:13.832+00,19,,2024-02-06 23:10:13.832+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Even more efficient quantum computations of chemistry through tensor hypercontraction,747,Physical simulation
+1597,f,T-count,13000000000,2024-02-06 23:09:27.598+00,2024-02-06 23:09:27.598+00,,19,1018,1066,2020-07-28,"Structure I, 52 orbitals",,,,,,,,1066,2024-02-06 23:08:44.062+00,2024-02-06 23:08:44.062+00,,19,746,208,746,Quantum computing enhanced computational catalysis,quantum computing enhanced computational catalysis,"The quantum computation of electronic energies can break the curse of dimensionality that plagues many-particle quantum mechanics. It is for this reason that a universal quantum computer has the potential to fundamentally change computational chemistry and materials science, areas in which strong electron correlations present severe hurdles for traditional electronic structure methods. Here, we present a state-of-the-art analysis of accurate energy measurements on a quantum computer for computational catalysis, using improved quantum algorithms with more than an order of magnitude improvement over the best previous algorithms. As a prototypical example of local catalytic chemical reactivity we consider the case of a ruthenium catalyst that can bind, activate, and transform carbon dioxide to the high-value chemical methanol. We aim at accurate resource estimates for the quantum computing steps required for assessing the electronic energy of key intermediates and transition states of its catalytic cycle. In particular, we present new quantum algorithms for double-factorized representations of the four-index integrals that can significantly reduce the computational cost over previous algorithms, and we discuss the challenges of increasing active space sizes to accurately deal with dynamical correlations. We address the requirements for future quantum hardware in order to make a universal quantum computer a successful and reliable tool for quantum computing enhanced computational materials science and chemistry, and identify open questions for further research.",https://arxiv.org/abs/2007.14460,"",,2024-02-06 23:08:23.574+00,2024-02-06 23:08:23.574+00,19,,2024-02-06 23:08:23.574+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Quantum computing enhanced computational catalysis,746,Physical simulation
+1596,t,Qubits,3400,2024-02-06 23:09:07.078+00,2024-02-06 23:09:07.078+00,,19,1018,1066,2020-07-28,"Structure I, 52 orbitals",,,,,,,,1066,2024-02-06 23:08:44.062+00,2024-02-06 23:08:44.062+00,,19,746,208,746,Quantum computing enhanced computational catalysis,quantum computing enhanced computational catalysis,"The quantum computation of electronic energies can break the curse of dimensionality that plagues many-particle quantum mechanics. It is for this reason that a universal quantum computer has the potential to fundamentally change computational chemistry and materials science, areas in which strong electron correlations present severe hurdles for traditional electronic structure methods. Here, we present a state-of-the-art analysis of accurate energy measurements on a quantum computer for computational catalysis, using improved quantum algorithms with more than an order of magnitude improvement over the best previous algorithms. As a prototypical example of local catalytic chemical reactivity we consider the case of a ruthenium catalyst that can bind, activate, and transform carbon dioxide to the high-value chemical methanol. We aim at accurate resource estimates for the quantum computing steps required for assessing the electronic energy of key intermediates and transition states of its catalytic cycle. In particular, we present new quantum algorithms for double-factorized representations of the four-index integrals that can significantly reduce the computational cost over previous algorithms, and we discuss the challenges of increasing active space sizes to accurately deal with dynamical correlations. We address the requirements for future quantum hardware in order to make a universal quantum computer a successful and reliable tool for quantum computing enhanced computational materials science and chemistry, and identify open questions for further research.",https://arxiv.org/abs/2007.14460,"",,2024-02-06 23:08:23.574+00,2024-02-06 23:08:23.574+00,19,,2024-02-06 23:08:23.574+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Quantum computing enhanced computational catalysis,746,Physical simulation
+1595,t,Qubits,437,2024-02-06 23:07:49.043+00,2024-02-06 23:07:49.043+00,,19,1017,1065,2019-02-06,Dirty ancilla algorithm; LLCUC orbitals,,,,,,,,1065,2024-02-06 23:06:17.311+00,2024-02-06 23:06:17.311+00,,19,745,208,745,Qubitization of Arbitrary Basis Quantum Chemistry Leveraging Sparsity and Low Rank Factorization,qubitization of arbitrary basis quantum chemistry leveraging sparsity and low rank factorization,"Recent work has dramatically reduced the gate complexity required to quantum simulate chemistry by using linear combinations of unitaries based methods to exploit structure in the plane wave basis Coulomb operator. Here, we show that one can achieve similar scaling even for arbitrary basis sets (which can be hundreds of times more compact than plane waves) by using qubitized quantum walks in a fashion that takes advantage of structure in the Coulomb operator, either by directly exploiting sparseness, or via a low rank tensor factorization. We provide circuits for several variants of our algorithm (which all improve over the scaling of prior methods) including one with $\widetilde{\cal O}(N^{3/2} λ)$ T complexity, where $N$ is number of orbitals and $λ$ is the 1-norm of the chemistry Hamiltonian. We deploy our algorithms to simulate the FeMoco molecule (relevant to Nitrogen fixation) and obtain circuits requiring about seven hundred times less surface code spacetime volume than prior quantum algorithms for this system, despite us using a larger and more accurate active space.",https://arxiv.org/abs/1902.02134,"",,2024-02-06 23:05:47.906+00,2024-02-06 23:05:47.906+00,19,,2024-02-06 23:05:47.906+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Qubitization of Arbitrary Basis Quantum Chemistry Leveraging Sparsity and Low Rank Factorization,745,Physical simulation
+1594,f,T-count,20000000000000,2024-02-06 23:07:32.857+00,2024-02-06 23:07:32.857+00,,19,1017,1065,2019-02-06,Dirty ancilla algorithm; LLCUC orbitals,,,,,,,,1065,2024-02-06 23:06:17.311+00,2024-02-06 23:06:17.311+00,,19,745,208,745,Qubitization of Arbitrary Basis Quantum Chemistry Leveraging Sparsity and Low Rank Factorization,qubitization of arbitrary basis quantum chemistry leveraging sparsity and low rank factorization,"Recent work has dramatically reduced the gate complexity required to quantum simulate chemistry by using linear combinations of unitaries based methods to exploit structure in the plane wave basis Coulomb operator. Here, we show that one can achieve similar scaling even for arbitrary basis sets (which can be hundreds of times more compact than plane waves) by using qubitized quantum walks in a fashion that takes advantage of structure in the Coulomb operator, either by directly exploiting sparseness, or via a low rank tensor factorization. We provide circuits for several variants of our algorithm (which all improve over the scaling of prior methods) including one with $\widetilde{\cal O}(N^{3/2} λ)$ T complexity, where $N$ is number of orbitals and $λ$ is the 1-norm of the chemistry Hamiltonian. We deploy our algorithms to simulate the FeMoco molecule (relevant to Nitrogen fixation) and obtain circuits requiring about seven hundred times less surface code spacetime volume than prior quantum algorithms for this system, despite us using a larger and more accurate active space.",https://arxiv.org/abs/1902.02134,"",,2024-02-06 23:05:47.906+00,2024-02-06 23:05:47.906+00,19,,2024-02-06 23:05:47.906+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Qubitization of Arbitrary Basis Quantum Chemistry Leveraging Sparsity and Low Rank Factorization,745,Physical simulation
+1593,f,T-count,21000000000000,2024-02-06 23:07:05.501+00,2024-02-06 23:07:05.501+00,,19,1017,1065,2019-02-06,Dirty ancilla algorithm; RWSWT orbitals,,,,,,,,1065,2024-02-06 23:06:17.311+00,2024-02-06 23:06:17.311+00,,19,745,208,745,Qubitization of Arbitrary Basis Quantum Chemistry Leveraging Sparsity and Low Rank Factorization,qubitization of arbitrary basis quantum chemistry leveraging sparsity and low rank factorization,"Recent work has dramatically reduced the gate complexity required to quantum simulate chemistry by using linear combinations of unitaries based methods to exploit structure in the plane wave basis Coulomb operator. Here, we show that one can achieve similar scaling even for arbitrary basis sets (which can be hundreds of times more compact than plane waves) by using qubitized quantum walks in a fashion that takes advantage of structure in the Coulomb operator, either by directly exploiting sparseness, or via a low rank tensor factorization. We provide circuits for several variants of our algorithm (which all improve over the scaling of prior methods) including one with $\widetilde{\cal O}(N^{3/2} λ)$ T complexity, where $N$ is number of orbitals and $λ$ is the 1-norm of the chemistry Hamiltonian. We deploy our algorithms to simulate the FeMoco molecule (relevant to Nitrogen fixation) and obtain circuits requiring about seven hundred times less surface code spacetime volume than prior quantum algorithms for this system, despite us using a larger and more accurate active space.",https://arxiv.org/abs/1902.02134,"",,2024-02-06 23:05:47.906+00,2024-02-06 23:05:47.906+00,19,,2024-02-06 23:05:47.906+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Qubitization of Arbitrary Basis Quantum Chemistry Leveraging Sparsity and Low Rank Factorization,745,Physical simulation
+1592,t,Qubits,378,2024-02-06 23:06:43.553+00,2024-02-06 23:06:43.553+00,,19,1017,1065,2019-02-06,Dirty ancilla algorithm; RWSWT orbitals,,,,,,,,1065,2024-02-06 23:06:17.311+00,2024-02-06 23:06:17.311+00,,19,745,208,745,Qubitization of Arbitrary Basis Quantum Chemistry Leveraging Sparsity and Low Rank Factorization,qubitization of arbitrary basis quantum chemistry leveraging sparsity and low rank factorization,"Recent work has dramatically reduced the gate complexity required to quantum simulate chemistry by using linear combinations of unitaries based methods to exploit structure in the plane wave basis Coulomb operator. Here, we show that one can achieve similar scaling even for arbitrary basis sets (which can be hundreds of times more compact than plane waves) by using qubitized quantum walks in a fashion that takes advantage of structure in the Coulomb operator, either by directly exploiting sparseness, or via a low rank tensor factorization. We provide circuits for several variants of our algorithm (which all improve over the scaling of prior methods) including one with $\widetilde{\cal O}(N^{3/2} λ)$ T complexity, where $N$ is number of orbitals and $λ$ is the 1-norm of the chemistry Hamiltonian. We deploy our algorithms to simulate the FeMoco molecule (relevant to Nitrogen fixation) and obtain circuits requiring about seven hundred times less surface code spacetime volume than prior quantum algorithms for this system, despite us using a larger and more accurate active space.",https://arxiv.org/abs/1902.02134,"",,2024-02-06 23:05:47.906+00,2024-02-06 23:05:47.906+00,19,,2024-02-06 23:05:47.906+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Qubitization of Arbitrary Basis Quantum Chemistry Leveraging Sparsity and Low Rank Factorization,745,Physical simulation
+1591,f,T-count,330000000000000,2024-02-06 23:04:50.945+00,2024-02-06 23:04:50.945+00,,19,1016,1064,2016-05-11,Qualitative accuracy,,,,,,,,1064,2024-02-06 23:03:09.814+00,2024-02-06 23:03:09.814+00,,19,744,208,744,Elucidating Reaction Mechanisms on Quantum Computers,elucidating reaction mechanisms on quantum computers,"We show how a quantum computer can be employed to elucidate reaction mechanisms in complex chemical systems, using the open problem of biological nitrogen fixation in nitrogenase as an example. We discuss how quantum computers can augment classical-computer simulations for such problems, to significantly increase their accuracy and enable hitherto intractable simulations. Detailed resource estimates show that, even when taking into account the substantial overhead of quantum error correction, and the need to compile into discrete gate sets, the necessary computations can be performed in reasonable time on small quantum computers. This demonstrates that quantum computers will realistically be able to tackle important problems in chemistry that are both scientifically and economically significant.",https://arxiv.org/abs/1605.03590,"",,2024-02-06 23:02:40.301+00,2024-02-06 23:02:40.301+00,19,,2024-02-06 23:02:40.3+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Elucidating Reaction Mechanisms on Quantum Computers,744,Physical simulation
+1590,t,Qubits,135,2024-02-06 23:04:28.742+00,2024-02-06 23:04:28.742+00,,19,1016,1064,2016-05-11,Qualitative accuracy,,,,,,,,1064,2024-02-06 23:03:09.814+00,2024-02-06 23:03:09.814+00,,19,744,208,744,Elucidating Reaction Mechanisms on Quantum Computers,elucidating reaction mechanisms on quantum computers,"We show how a quantum computer can be employed to elucidate reaction mechanisms in complex chemical systems, using the open problem of biological nitrogen fixation in nitrogenase as an example. We discuss how quantum computers can augment classical-computer simulations for such problems, to significantly increase their accuracy and enable hitherto intractable simulations. Detailed resource estimates show that, even when taking into account the substantial overhead of quantum error correction, and the need to compile into discrete gate sets, the necessary computations can be performed in reasonable time on small quantum computers. This demonstrates that quantum computers will realistically be able to tackle important problems in chemistry that are both scientifically and economically significant.",https://arxiv.org/abs/1605.03590,"",,2024-02-06 23:02:40.301+00,2024-02-06 23:02:40.301+00,19,,2024-02-06 23:02:40.3+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Elucidating Reaction Mechanisms on Quantum Computers,744,Physical simulation
+1589,f,T-count,3.5e+15,2024-02-06 23:04:05+00,2024-02-06 23:04:05+00,,19,1016,1064,2016-05-11,Quantitative accuracy,,,,,,,,1064,2024-02-06 23:03:09.814+00,2024-02-06 23:03:09.814+00,,19,744,208,744,Elucidating Reaction Mechanisms on Quantum Computers,elucidating reaction mechanisms on quantum computers,"We show how a quantum computer can be employed to elucidate reaction mechanisms in complex chemical systems, using the open problem of biological nitrogen fixation in nitrogenase as an example. We discuss how quantum computers can augment classical-computer simulations for such problems, to significantly increase their accuracy and enable hitherto intractable simulations. Detailed resource estimates show that, even when taking into account the substantial overhead of quantum error correction, and the need to compile into discrete gate sets, the necessary computations can be performed in reasonable time on small quantum computers. This demonstrates that quantum computers will realistically be able to tackle important problems in chemistry that are both scientifically and economically significant.",https://arxiv.org/abs/1605.03590,"",,2024-02-06 23:02:40.301+00,2024-02-06 23:02:40.301+00,19,,2024-02-06 23:02:40.3+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Elucidating Reaction Mechanisms on Quantum Computers,744,Physical simulation
+1588,t,Qubits,135,2024-02-06 23:03:41.578+00,2024-02-06 23:03:41.578+00,,19,1016,1064,2016-05-11,Quantitative accuracy,,,,,,,,1064,2024-02-06 23:03:09.814+00,2024-02-06 23:03:09.814+00,,19,744,208,744,Elucidating Reaction Mechanisms on Quantum Computers,elucidating reaction mechanisms on quantum computers,"We show how a quantum computer can be employed to elucidate reaction mechanisms in complex chemical systems, using the open problem of biological nitrogen fixation in nitrogenase as an example. We discuss how quantum computers can augment classical-computer simulations for such problems, to significantly increase their accuracy and enable hitherto intractable simulations. Detailed resource estimates show that, even when taking into account the substantial overhead of quantum error correction, and the need to compile into discrete gate sets, the necessary computations can be performed in reasonable time on small quantum computers. This demonstrates that quantum computers will realistically be able to tackle important problems in chemistry that are both scientifically and economically significant.",https://arxiv.org/abs/1605.03590,"",,2024-02-06 23:02:40.301+00,2024-02-06 23:02:40.301+00,19,,2024-02-06 23:02:40.3+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Elucidating Reaction Mechanisms on Quantum Computers,744,Physical simulation
+1587,f,T-count,4000000000000,2024-02-06 23:02:03.434+00,2024-02-06 23:02:03.434+00,,19,1015,1063,2023-12-08,Compact encoding,,,,,,,,1063,2024-02-06 23:00:12.602+00,2024-02-06 23:00:12.602+00,,19,743,208,743,Quantum Algorithms for Simulating Nuclear Effective Field Theories,quantum algorithms for simulating nuclear effective field theories,"Quantum computers offer the potential to simulate nuclear processes that are classically intractable. With the goal of understanding the necessary quantum resources, we employ state-of-the-art Hamiltonian-simulation methods, and conduct a thorough algorithmic analysis, to estimate the qubit and gate costs to simulate low-energy effective field theories (EFTs) of nuclear physics. In particular, within the framework of nuclear lattice EFT, we obtain simulation costs for the leading-order pionless and pionful EFTs. We consider both static pions represented by a one-pion-exchange potential between the nucleons, and dynamical pions represented by relativistic bosonic fields coupled to non-relativistic nucleons. We examine the resource costs for the tasks of time evolution and energy estimation for physically relevant scales. We account for model errors associated with truncating either long-range interactions in the one-pion-exchange EFT or the pionic Hilbert space in the dynamical-pion EFT, and for algorithmic errors associated with product-formula approximations and quantum phase estimation. Our results show that the pionless EFT is the least costly to simulate and the dynamical-pion theory is the costliest. We demonstrate how symmetries of the low-energy nuclear Hamiltonians can be utilized to obtain tighter error bounds on the simulation algorithm. By retaining the locality of nucleonic interactions when mapped to qubits, we achieve reduced circuit depth and substantial parallelization. We further develop new methods to bound the algorithmic error for classes of fermionic Hamiltonians that preserve the number of fermions, and demonstrate that reasonably tight Trotter error bounds can be achieved by explicitly computing nested commutators of Hamiltonian terms. This work highlights the importance of combining physics insights and algorithmic advancement in reducing quantum-simulation costs.",https://arxiv.org/abs/2312.05344,"",,2024-02-06 22:56:28.017+00,2024-02-06 22:56:28.017+00,19,,2024-02-06 22:56:28.016+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Quantum Algorithms for Simulating Nuclear Effective Field Theories,743,Physical simulation
+1586,f,T-count,4000000000000,2024-02-06 23:01:46.306+00,2024-02-06 23:01:46.306+00,,19,1015,1063,2023-12-08,Compact encoding,,,,,,,,1063,2024-02-06 23:00:12.602+00,2024-02-06 23:00:12.602+00,,19,743,208,743,Quantum Algorithms for Simulating Nuclear Effective Field Theories,quantum algorithms for simulating nuclear effective field theories,"Quantum computers offer the potential to simulate nuclear processes that are classically intractable. With the goal of understanding the necessary quantum resources, we employ state-of-the-art Hamiltonian-simulation methods, and conduct a thorough algorithmic analysis, to estimate the qubit and gate costs to simulate low-energy effective field theories (EFTs) of nuclear physics. In particular, within the framework of nuclear lattice EFT, we obtain simulation costs for the leading-order pionless and pionful EFTs. We consider both static pions represented by a one-pion-exchange potential between the nucleons, and dynamical pions represented by relativistic bosonic fields coupled to non-relativistic nucleons. We examine the resource costs for the tasks of time evolution and energy estimation for physically relevant scales. We account for model errors associated with truncating either long-range interactions in the one-pion-exchange EFT or the pionic Hilbert space in the dynamical-pion EFT, and for algorithmic errors associated with product-formula approximations and quantum phase estimation. Our results show that the pionless EFT is the least costly to simulate and the dynamical-pion theory is the costliest. We demonstrate how symmetries of the low-energy nuclear Hamiltonians can be utilized to obtain tighter error bounds on the simulation algorithm. By retaining the locality of nucleonic interactions when mapped to qubits, we achieve reduced circuit depth and substantial parallelization. We further develop new methods to bound the algorithmic error for classes of fermionic Hamiltonians that preserve the number of fermions, and demonstrate that reasonably tight Trotter error bounds can be achieved by explicitly computing nested commutators of Hamiltonian terms. This work highlights the importance of combining physics insights and algorithmic advancement in reducing quantum-simulation costs.",https://arxiv.org/abs/2312.05344,"",,2024-02-06 22:56:28.017+00,2024-02-06 22:56:28.017+00,19,,2024-02-06 22:56:28.016+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Quantum Algorithms for Simulating Nuclear Effective Field Theories,743,Physical simulation
+1585,t,Qubits,10000,2024-02-06 23:01:29.839+00,2024-02-06 23:01:29.839+00,,19,1015,1063,2023-12-08,Compact encoding,,,,,,,,1063,2024-02-06 23:00:12.602+00,2024-02-06 23:00:12.602+00,,19,743,208,743,Quantum Algorithms for Simulating Nuclear Effective Field Theories,quantum algorithms for simulating nuclear effective field theories,"Quantum computers offer the potential to simulate nuclear processes that are classically intractable. With the goal of understanding the necessary quantum resources, we employ state-of-the-art Hamiltonian-simulation methods, and conduct a thorough algorithmic analysis, to estimate the qubit and gate costs to simulate low-energy effective field theories (EFTs) of nuclear physics. In particular, within the framework of nuclear lattice EFT, we obtain simulation costs for the leading-order pionless and pionful EFTs. We consider both static pions represented by a one-pion-exchange potential between the nucleons, and dynamical pions represented by relativistic bosonic fields coupled to non-relativistic nucleons. We examine the resource costs for the tasks of time evolution and energy estimation for physically relevant scales. We account for model errors associated with truncating either long-range interactions in the one-pion-exchange EFT or the pionic Hilbert space in the dynamical-pion EFT, and for algorithmic errors associated with product-formula approximations and quantum phase estimation. Our results show that the pionless EFT is the least costly to simulate and the dynamical-pion theory is the costliest. We demonstrate how symmetries of the low-energy nuclear Hamiltonians can be utilized to obtain tighter error bounds on the simulation algorithm. By retaining the locality of nucleonic interactions when mapped to qubits, we achieve reduced circuit depth and substantial parallelization. We further develop new methods to bound the algorithmic error for classes of fermionic Hamiltonians that preserve the number of fermions, and demonstrate that reasonably tight Trotter error bounds can be achieved by explicitly computing nested commutators of Hamiltonian terms. This work highlights the importance of combining physics insights and algorithmic advancement in reducing quantum-simulation costs.",https://arxiv.org/abs/2312.05344,"",,2024-02-06 22:56:28.017+00,2024-02-06 22:56:28.017+00,19,,2024-02-06 22:56:28.016+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Quantum Algorithms for Simulating Nuclear Effective Field Theories,743,Physical simulation
+1584,f,T-count,4000000000000,2024-02-06 23:01:04.903+00,2024-02-06 23:01:04.903+00,,19,1015,1063,2023-12-08,VC encoding,,,,,,,,1063,2024-02-06 23:00:12.602+00,2024-02-06 23:00:12.602+00,,19,743,208,743,Quantum Algorithms for Simulating Nuclear Effective Field Theories,quantum algorithms for simulating nuclear effective field theories,"Quantum computers offer the potential to simulate nuclear processes that are classically intractable. With the goal of understanding the necessary quantum resources, we employ state-of-the-art Hamiltonian-simulation methods, and conduct a thorough algorithmic analysis, to estimate the qubit and gate costs to simulate low-energy effective field theories (EFTs) of nuclear physics. In particular, within the framework of nuclear lattice EFT, we obtain simulation costs for the leading-order pionless and pionful EFTs. We consider both static pions represented by a one-pion-exchange potential between the nucleons, and dynamical pions represented by relativistic bosonic fields coupled to non-relativistic nucleons. We examine the resource costs for the tasks of time evolution and energy estimation for physically relevant scales. We account for model errors associated with truncating either long-range interactions in the one-pion-exchange EFT or the pionic Hilbert space in the dynamical-pion EFT, and for algorithmic errors associated with product-formula approximations and quantum phase estimation. Our results show that the pionless EFT is the least costly to simulate and the dynamical-pion theory is the costliest. We demonstrate how symmetries of the low-energy nuclear Hamiltonians can be utilized to obtain tighter error bounds on the simulation algorithm. By retaining the locality of nucleonic interactions when mapped to qubits, we achieve reduced circuit depth and substantial parallelization. We further develop new methods to bound the algorithmic error for classes of fermionic Hamiltonians that preserve the number of fermions, and demonstrate that reasonably tight Trotter error bounds can be achieved by explicitly computing nested commutators of Hamiltonian terms. This work highlights the importance of combining physics insights and algorithmic advancement in reducing quantum-simulation costs.",https://arxiv.org/abs/2312.05344,"",,2024-02-06 22:56:28.017+00,2024-02-06 22:56:28.017+00,19,,2024-02-06 22:56:28.016+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Quantum Algorithms for Simulating Nuclear Effective Field Theories,743,Physical simulation
+1583,t,Qubits,6000,2024-02-06 23:00:45.586+00,2024-02-06 23:00:45.586+00,,19,1015,1063,2023-12-08,VC encoding,,,,,,,,1063,2024-02-06 23:00:12.602+00,2024-02-06 23:00:12.602+00,,19,743,208,743,Quantum Algorithms for Simulating Nuclear Effective Field Theories,quantum algorithms for simulating nuclear effective field theories,"Quantum computers offer the potential to simulate nuclear processes that are classically intractable. With the goal of understanding the necessary quantum resources, we employ state-of-the-art Hamiltonian-simulation methods, and conduct a thorough algorithmic analysis, to estimate the qubit and gate costs to simulate low-energy effective field theories (EFTs) of nuclear physics. In particular, within the framework of nuclear lattice EFT, we obtain simulation costs for the leading-order pionless and pionful EFTs. We consider both static pions represented by a one-pion-exchange potential between the nucleons, and dynamical pions represented by relativistic bosonic fields coupled to non-relativistic nucleons. We examine the resource costs for the tasks of time evolution and energy estimation for physically relevant scales. We account for model errors associated with truncating either long-range interactions in the one-pion-exchange EFT or the pionic Hilbert space in the dynamical-pion EFT, and for algorithmic errors associated with product-formula approximations and quantum phase estimation. Our results show that the pionless EFT is the least costly to simulate and the dynamical-pion theory is the costliest. We demonstrate how symmetries of the low-energy nuclear Hamiltonians can be utilized to obtain tighter error bounds on the simulation algorithm. By retaining the locality of nucleonic interactions when mapped to qubits, we achieve reduced circuit depth and substantial parallelization. We further develop new methods to bound the algorithmic error for classes of fermionic Hamiltonians that preserve the number of fermions, and demonstrate that reasonably tight Trotter error bounds can be achieved by explicitly computing nested commutators of Hamiltonian terms. This work highlights the importance of combining physics insights and algorithmic advancement in reducing quantum-simulation costs.",https://arxiv.org/abs/2312.05344,"",,2024-02-06 22:56:28.017+00,2024-02-06 22:56:28.017+00,19,,2024-02-06 22:56:28.016+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Quantum Algorithms for Simulating Nuclear Effective Field Theories,743,Physical simulation
+1582,f,T-count,200000000000,2024-02-06 22:50:43.961+00,2024-02-06 22:50:43.961+00,,19,1013,1062,2017-11-29,"",,,,,,,,1062,2024-02-06 22:49:05.869+00,2024-02-06 22:49:05.869+00,,19,742,208,742,Toward the first quantum simulation with quantum speedup,toward the first quantum simulation with quantum speedup,"With quantum computers of significant size now on the horizon, we should understand how to best exploit their initially limited abilities. To this end, we aim to identify a practical problem that is beyond the reach of current classical computers, but that requires the fewest resources for a quantum computer. We consider quantum simulation of spin systems, which could be applied to understand condensed matter phenomena. We synthesize explicit circuits for three leading quantum simulation algorithms, employing diverse techniques to tighten error bounds and optimize circuit implementations. Quantum signal processing appears to be preferred among algorithms with rigorous performance guarantees, whereas higher-order product formulas prevail if empirical error estimates suffice. Our circuits are orders of magnitude smaller than those for the simplest classically-infeasible instances of factoring and quantum chemistry.",https://arxiv.org/abs/1711.10980,"",,2024-02-06 22:48:43.609+00,2024-02-06 22:48:43.609+00,19,,2024-02-06 22:48:43.608+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Toward the first quantum simulation with quantum speedup,742,Physical simulation
+1581,f,T-count,20000000000,2024-02-06 22:50:31.895+00,2024-02-06 22:50:31.895+00,,19,1013,1062,2017-11-29,"",,,,,,,,1062,2024-02-06 22:49:05.869+00,2024-02-06 22:49:05.869+00,,19,742,208,742,Toward the first quantum simulation with quantum speedup,toward the first quantum simulation with quantum speedup,"With quantum computers of significant size now on the horizon, we should understand how to best exploit their initially limited abilities. To this end, we aim to identify a practical problem that is beyond the reach of current classical computers, but that requires the fewest resources for a quantum computer. We consider quantum simulation of spin systems, which could be applied to understand condensed matter phenomena. We synthesize explicit circuits for three leading quantum simulation algorithms, employing diverse techniques to tighten error bounds and optimize circuit implementations. Quantum signal processing appears to be preferred among algorithms with rigorous performance guarantees, whereas higher-order product formulas prevail if empirical error estimates suffice. Our circuits are orders of magnitude smaller than those for the simplest classically-infeasible instances of factoring and quantum chemistry.",https://arxiv.org/abs/1711.10980,"",,2024-02-06 22:48:43.609+00,2024-02-06 22:48:43.609+00,19,,2024-02-06 22:48:43.608+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Toward the first quantum simulation with quantum speedup,742,Physical simulation
+1580,f,T-count,1000000000000,2024-02-06 22:50:04.065+00,2024-02-06 22:50:04.065+00,,19,1013,1062,2017-11-29,"",,,,,,,,1062,2024-02-06 22:49:05.869+00,2024-02-06 22:49:05.869+00,,19,742,208,742,Toward the first quantum simulation with quantum speedup,toward the first quantum simulation with quantum speedup,"With quantum computers of significant size now on the horizon, we should understand how to best exploit their initially limited abilities. To this end, we aim to identify a practical problem that is beyond the reach of current classical computers, but that requires the fewest resources for a quantum computer. We consider quantum simulation of spin systems, which could be applied to understand condensed matter phenomena. We synthesize explicit circuits for three leading quantum simulation algorithms, employing diverse techniques to tighten error bounds and optimize circuit implementations. Quantum signal processing appears to be preferred among algorithms with rigorous performance guarantees, whereas higher-order product formulas prevail if empirical error estimates suffice. Our circuits are orders of magnitude smaller than those for the simplest classically-infeasible instances of factoring and quantum chemistry.",https://arxiv.org/abs/1711.10980,"",,2024-02-06 22:48:43.609+00,2024-02-06 22:48:43.609+00,19,,2024-02-06 22:48:43.608+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Toward the first quantum simulation with quantum speedup,742,Physical simulation
+1579,t,Qubits,250,2024-02-06 22:49:42.837+00,2024-02-06 22:49:42.837+00,,19,1013,1062,2017-11-29,"",,,,,,,,1062,2024-02-06 22:49:05.869+00,2024-02-06 22:49:05.869+00,,19,742,208,742,Toward the first quantum simulation with quantum speedup,toward the first quantum simulation with quantum speedup,"With quantum computers of significant size now on the horizon, we should understand how to best exploit their initially limited abilities. To this end, we aim to identify a practical problem that is beyond the reach of current classical computers, but that requires the fewest resources for a quantum computer. We consider quantum simulation of spin systems, which could be applied to understand condensed matter phenomena. We synthesize explicit circuits for three leading quantum simulation algorithms, employing diverse techniques to tighten error bounds and optimize circuit implementations. Quantum signal processing appears to be preferred among algorithms with rigorous performance guarantees, whereas higher-order product formulas prevail if empirical error estimates suffice. Our circuits are orders of magnitude smaller than those for the simplest classically-infeasible instances of factoring and quantum chemistry.",https://arxiv.org/abs/1711.10980,"",,2024-02-06 22:48:43.609+00,2024-02-06 22:48:43.609+00,19,,2024-02-06 22:48:43.608+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Toward the first quantum simulation with quantum speedup,742,Physical simulation
+1578,t,Qubits,120,2024-02-06 22:49:36.293+00,2024-02-06 22:49:36.293+00,,19,1013,1062,2017-11-29,"",,,,,,,,1062,2024-02-06 22:49:05.869+00,2024-02-06 22:49:05.869+00,,19,742,208,742,Toward the first quantum simulation with quantum speedup,toward the first quantum simulation with quantum speedup,"With quantum computers of significant size now on the horizon, we should understand how to best exploit their initially limited abilities. To this end, we aim to identify a practical problem that is beyond the reach of current classical computers, but that requires the fewest resources for a quantum computer. We consider quantum simulation of spin systems, which could be applied to understand condensed matter phenomena. We synthesize explicit circuits for three leading quantum simulation algorithms, employing diverse techniques to tighten error bounds and optimize circuit implementations. Quantum signal processing appears to be preferred among algorithms with rigorous performance guarantees, whereas higher-order product formulas prevail if empirical error estimates suffice. Our circuits are orders of magnitude smaller than those for the simplest classically-infeasible instances of factoring and quantum chemistry.",https://arxiv.org/abs/1711.10980,"",,2024-02-06 22:48:43.609+00,2024-02-06 22:48:43.609+00,19,,2024-02-06 22:48:43.608+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Toward the first quantum simulation with quantum speedup,742,Physical simulation
+1577,t,Qubits,100,2024-02-06 22:49:24.192+00,2024-02-06 22:49:24.192+00,,19,1013,1062,2017-11-29,"",,,,,,,,1062,2024-02-06 22:49:05.869+00,2024-02-06 22:49:05.869+00,,19,742,208,742,Toward the first quantum simulation with quantum speedup,toward the first quantum simulation with quantum speedup,"With quantum computers of significant size now on the horizon, we should understand how to best exploit their initially limited abilities. To this end, we aim to identify a practical problem that is beyond the reach of current classical computers, but that requires the fewest resources for a quantum computer. We consider quantum simulation of spin systems, which could be applied to understand condensed matter phenomena. We synthesize explicit circuits for three leading quantum simulation algorithms, employing diverse techniques to tighten error bounds and optimize circuit implementations. Quantum signal processing appears to be preferred among algorithms with rigorous performance guarantees, whereas higher-order product formulas prevail if empirical error estimates suffice. Our circuits are orders of magnitude smaller than those for the simplest classically-infeasible instances of factoring and quantum chemistry.",https://arxiv.org/abs/1711.10980,"",,2024-02-06 22:48:43.609+00,2024-02-06 22:48:43.609+00,19,,2024-02-06 22:48:43.608+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Toward the first quantum simulation with quantum speedup,742,Physical simulation
+1576,f,T-count,7200000,2024-02-06 22:46:22.732+00,2024-02-06 22:46:22.732+00,,19,1012,1061,2018-05-12,"",,,,,,,,1061,2024-02-06 22:43:57.63+00,2024-02-06 22:43:57.63+00,,19,741,208,741,Low cost quantum circuits for classically intractable instances of the Hamiltonian dynamics simulation problem,low cost quantum circuits for classically intractable instances of the hamiltonian dynamics simulation problem,"We develop circuit implementations for digital-level quantum Hamiltonian dynamics simulation algorithms suitable for implementation on a reconfigurable quantum computer, such as trapped ions. Our focus is on the co-design of a problem, its solution, and quantum hardware capable of executing the solution at the minimal cost expressed in terms of the quantum computing resources used while demonstrating the solution of an instance of a scientifically interesting problem that is intractable classically. The choice for Hamiltonian dynamics simulation is due to the combination of its usefulness in the study of equilibrium in closed quantum mechanical systems, a low cost in the implementation by quantum algorithms, and the difficulty of classical simulation. By targeting a specific type of quantum computer and tailoring the problem instance and solution to suit physical constraints imposed by the hardware, we are able to reduce the resource counts by a factor of $10$ in a physical-level implementation and a factor of $30$ to $60$ in a fault-tolerant implementation over state of the art.",https://arxiv.org/abs/1805.04645,"",,2024-02-06 22:43:27.47+00,2024-02-06 22:43:27.47+00,19,,2024-02-06 22:43:27.469+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Low cost quantum circuits for classically intractable instances of the Hamiltonian dynamics simulation problem,741,Physical simulation
+1575,t,Qubits,101,2024-02-06 22:46:03.302+00,2024-02-06 22:46:03.302+00,,19,1012,1061,2018-05-12,"",,,,,,,,1061,2024-02-06 22:43:57.63+00,2024-02-06 22:43:57.63+00,,19,741,208,741,Low cost quantum circuits for classically intractable instances of the Hamiltonian dynamics simulation problem,low cost quantum circuits for classically intractable instances of the hamiltonian dynamics simulation problem,"We develop circuit implementations for digital-level quantum Hamiltonian dynamics simulation algorithms suitable for implementation on a reconfigurable quantum computer, such as trapped ions. Our focus is on the co-design of a problem, its solution, and quantum hardware capable of executing the solution at the minimal cost expressed in terms of the quantum computing resources used while demonstrating the solution of an instance of a scientifically interesting problem that is intractable classically. The choice for Hamiltonian dynamics simulation is due to the combination of its usefulness in the study of equilibrium in closed quantum mechanical systems, a low cost in the implementation by quantum algorithms, and the difficulty of classical simulation. By targeting a specific type of quantum computer and tailoring the problem instance and solution to suit physical constraints imposed by the hardware, we are able to reduce the resource counts by a factor of $10$ in a physical-level implementation and a factor of $30$ to $60$ in a fault-tolerant implementation over state of the art.",https://arxiv.org/abs/1805.04645,"",,2024-02-06 22:43:27.47+00,2024-02-06 22:43:27.47+00,19,,2024-02-06 22:43:27.469+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Low cost quantum circuits for classically intractable instances of the Hamiltonian dynamics simulation problem,741,Physical simulation
+1574,f,T-count,13000000,2024-02-06 22:45:38.71+00,2024-02-06 22:45:38.71+00,,19,1012,1061,2018-05-12,"",,,,,,,,1061,2024-02-06 22:43:57.63+00,2024-02-06 22:43:57.63+00,,19,741,208,741,Low cost quantum circuits for classically intractable instances of the Hamiltonian dynamics simulation problem,low cost quantum circuits for classically intractable instances of the hamiltonian dynamics simulation problem,"We develop circuit implementations for digital-level quantum Hamiltonian dynamics simulation algorithms suitable for implementation on a reconfigurable quantum computer, such as trapped ions. Our focus is on the co-design of a problem, its solution, and quantum hardware capable of executing the solution at the minimal cost expressed in terms of the quantum computing resources used while demonstrating the solution of an instance of a scientifically interesting problem that is intractable classically. The choice for Hamiltonian dynamics simulation is due to the combination of its usefulness in the study of equilibrium in closed quantum mechanical systems, a low cost in the implementation by quantum algorithms, and the difficulty of classical simulation. By targeting a specific type of quantum computer and tailoring the problem instance and solution to suit physical constraints imposed by the hardware, we are able to reduce the resource counts by a factor of $10$ in a physical-level implementation and a factor of $30$ to $60$ in a fault-tolerant implementation over state of the art.",https://arxiv.org/abs/1805.04645,"",,2024-02-06 22:43:27.47+00,2024-02-06 22:43:27.47+00,19,,2024-02-06 22:43:27.469+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Low cost quantum circuits for classically intractable instances of the Hamiltonian dynamics simulation problem,741,Physical simulation
+1573,f,T-count,61000000,2024-02-06 22:44:35.543+00,2024-02-06 22:44:35.543+00,,19,1012,1061,2018-05-12,"",,,,,,,,1061,2024-02-06 22:43:57.63+00,2024-02-06 22:43:57.63+00,,19,741,208,741,Low cost quantum circuits for classically intractable instances of the Hamiltonian dynamics simulation problem,low cost quantum circuits for classically intractable instances of the hamiltonian dynamics simulation problem,"We develop circuit implementations for digital-level quantum Hamiltonian dynamics simulation algorithms suitable for implementation on a reconfigurable quantum computer, such as trapped ions. Our focus is on the co-design of a problem, its solution, and quantum hardware capable of executing the solution at the minimal cost expressed in terms of the quantum computing resources used while demonstrating the solution of an instance of a scientifically interesting problem that is intractable classically. The choice for Hamiltonian dynamics simulation is due to the combination of its usefulness in the study of equilibrium in closed quantum mechanical systems, a low cost in the implementation by quantum algorithms, and the difficulty of classical simulation. By targeting a specific type of quantum computer and tailoring the problem instance and solution to suit physical constraints imposed by the hardware, we are able to reduce the resource counts by a factor of $10$ in a physical-level implementation and a factor of $30$ to $60$ in a fault-tolerant implementation over state of the art.",https://arxiv.org/abs/1805.04645,"",,2024-02-06 22:43:27.47+00,2024-02-06 22:43:27.47+00,19,,2024-02-06 22:43:27.469+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Low cost quantum circuits for classically intractable instances of the Hamiltonian dynamics simulation problem,741,Physical simulation
+1572,t,Qubits,131,2024-02-06 22:44:11.749+00,2024-02-06 22:44:11.749+00,,19,1012,1061,2018-05-12,"",,,,,,,,1061,2024-02-06 22:43:57.63+00,2024-02-06 22:43:57.63+00,,19,741,208,741,Low cost quantum circuits for classically intractable instances of the Hamiltonian dynamics simulation problem,low cost quantum circuits for classically intractable instances of the hamiltonian dynamics simulation problem,"We develop circuit implementations for digital-level quantum Hamiltonian dynamics simulation algorithms suitable for implementation on a reconfigurable quantum computer, such as trapped ions. Our focus is on the co-design of a problem, its solution, and quantum hardware capable of executing the solution at the minimal cost expressed in terms of the quantum computing resources used while demonstrating the solution of an instance of a scientifically interesting problem that is intractable classically. The choice for Hamiltonian dynamics simulation is due to the combination of its usefulness in the study of equilibrium in closed quantum mechanical systems, a low cost in the implementation by quantum algorithms, and the difficulty of classical simulation. By targeting a specific type of quantum computer and tailoring the problem instance and solution to suit physical constraints imposed by the hardware, we are able to reduce the resource counts by a factor of $10$ in a physical-level implementation and a factor of $30$ to $60$ in a fault-tolerant implementation over state of the art.",https://arxiv.org/abs/1805.04645,"",,2024-02-06 22:43:27.47+00,2024-02-06 22:43:27.47+00,19,,2024-02-06 22:43:27.469+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Low cost quantum circuits for classically intractable instances of the Hamiltonian dynamics simulation problem,741,Physical simulation
+1571,f,T-count,2400000,2024-02-06 22:42:09.425+00,2024-02-06 22:42:09.425+00,,19,1011,1060,2022-11-14,"",,,,,,,,1060,2024-02-06 22:41:18.829+00,2024-02-06 22:41:18.829+00,,19,701,208,701,Assessing requirements to scale to practical quantum advantage,assessing requirements to scale to practical quantum advantage,"While quantum computers promise to solve some scientifically and commercially valuable problems thought intractable for classical machines, delivering on this promise will require a large-scale quantum machine. Understanding the impact of architecture design choices for a scaled quantum stack for specific applications, prior to full realization of the quantum system, is an important open challenge. To this end, we develop a framework for quantum resource estimation, abstracting the layers of the stack, to estimate resources required across these layers for large-scale quantum applications. Using a tool that implements this framework, we assess three scaled quantum applications and find that hundreds of thousands to millions of physical qubits are needed to achieve practical quantum advantage. We identify three qubit parameters, namely size, speed, and controllability, that are critical at scale to rendering these applications practical. A goal of our work is to accelerate progress towards practical quantum advantage by enabling the broader community to explore design choices across the stack, from algorithms to qubits.",https://arxiv.org/abs/2211.07629,"",2023-11-30 17:31:29.88+00,2023-10-21 13:28:09.402+00,2023-11-30 17:31:29.88+00,19,,2023-10-21 13:28:09.402+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Assessing requirements to scale to practical quantum advantage,701,Physical simulation
+1570,t,Qubits,230,2024-02-06 22:41:37.084+00,2024-02-06 22:41:37.084+00,,19,1011,1060,2022-11-14,"",,,,,,,,1060,2024-02-06 22:41:18.829+00,2024-02-06 22:41:18.829+00,,19,701,208,701,Assessing requirements to scale to practical quantum advantage,assessing requirements to scale to practical quantum advantage,"While quantum computers promise to solve some scientifically and commercially valuable problems thought intractable for classical machines, delivering on this promise will require a large-scale quantum machine. Understanding the impact of architecture design choices for a scaled quantum stack for specific applications, prior to full realization of the quantum system, is an important open challenge. To this end, we develop a framework for quantum resource estimation, abstracting the layers of the stack, to estimate resources required across these layers for large-scale quantum applications. Using a tool that implements this framework, we assess three scaled quantum applications and find that hundreds of thousands to millions of physical qubits are needed to achieve practical quantum advantage. We identify three qubit parameters, namely size, speed, and controllability, that are critical at scale to rendering these applications practical. A goal of our work is to accelerate progress towards practical quantum advantage by enabling the broader community to explore design choices across the stack, from algorithms to qubits.",https://arxiv.org/abs/2211.07629,"",2023-11-30 17:31:29.88+00,2023-10-21 13:28:09.402+00,2023-11-30 17:31:29.88+00,19,,2023-10-21 13:28:09.402+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Assessing requirements to scale to practical quantum advantage,701,Physical simulation
+1569,f,Toffoli count,1300000,2024-02-06 22:10:23.877+00,2024-02-06 22:10:23.877+00,,19,1010,1059,2019-02-27,"",,,,,,,,1059,2024-02-06 22:08:40.718+00,2024-02-06 22:08:40.718+00,,19,740,208,740,Improved Fault-Tolerant Quantum Simulation of Condensed-Phase Correlated Electrons via Trotterization,improved fault-tolerant quantum simulation of condensed-phase correlated electrons via trotterization,"Recent work has deployed linear combinations of unitaries techniques to reduce the cost of fault-tolerant quantum simulations of correlated electron models. Here, we show that one can sometimes improve upon those results with optimized implementations of Trotter-Suzuki-based product formulas. We show that low-order Trotter methods perform surprisingly well when used with phase estimation to compute relative precision quantities (e.g. energies per unit cell), as is often the goal for condensed-phase systems. In this context, simulations of the Hubbard and plane-wave electronic structure models with $N < 10^5$ fermionic modes can be performed with roughly $O(1)$ and $O(N^2)$ T complexities. We perform numerics revealing tradeoffs between the error and gate complexity of a Trotter step; e.g., we show that split-operator techniques have less Trotter error than popular alternatives. By compiling to surface code fault-tolerant gates and assuming error rates of one part per thousand, we show that one can error-correct quantum simulations of interesting, classically intractable instances with a few hundred thousand physical qubits.",https://arxiv.org/abs/1902.10673,"",,2024-02-06 22:07:51.586+00,2024-02-06 22:07:51.586+00,19,,2024-02-06 22:07:51.585+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Improved Fault-Tolerant Quantum Simulation of Condensed-Phase Correlated Electrons via Trotterization,740,Physical simulation
+1568,f,T-count,92000000,2024-02-06 22:09:53.993+00,2024-02-06 22:09:53.993+00,,19,1010,1059,2019-02-27,"",,,,,,,,1059,2024-02-06 22:08:40.718+00,2024-02-06 22:08:40.718+00,,19,740,208,740,Improved Fault-Tolerant Quantum Simulation of Condensed-Phase Correlated Electrons via Trotterization,improved fault-tolerant quantum simulation of condensed-phase correlated electrons via trotterization,"Recent work has deployed linear combinations of unitaries techniques to reduce the cost of fault-tolerant quantum simulations of correlated electron models. Here, we show that one can sometimes improve upon those results with optimized implementations of Trotter-Suzuki-based product formulas. We show that low-order Trotter methods perform surprisingly well when used with phase estimation to compute relative precision quantities (e.g. energies per unit cell), as is often the goal for condensed-phase systems. In this context, simulations of the Hubbard and plane-wave electronic structure models with $N < 10^5$ fermionic modes can be performed with roughly $O(1)$ and $O(N^2)$ T complexities. We perform numerics revealing tradeoffs between the error and gate complexity of a Trotter step; e.g., we show that split-operator techniques have less Trotter error than popular alternatives. By compiling to surface code fault-tolerant gates and assuming error rates of one part per thousand, we show that one can error-correct quantum simulations of interesting, classically intractable instances with a few hundred thousand physical qubits.",https://arxiv.org/abs/1902.10673,"",,2024-02-06 22:07:51.586+00,2024-02-06 22:07:51.586+00,19,,2024-02-06 22:07:51.585+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Improved Fault-Tolerant Quantum Simulation of Condensed-Phase Correlated Electrons via Trotterization,740,Physical simulation
+1567,t,Qubits,204,2024-02-06 22:09:06.626+00,2024-02-06 22:09:06.626+00,,19,1010,1059,2019-02-27,"",,,,,,,,1059,2024-02-06 22:08:40.718+00,2024-02-06 22:08:40.718+00,,19,740,208,740,Improved Fault-Tolerant Quantum Simulation of Condensed-Phase Correlated Electrons via Trotterization,improved fault-tolerant quantum simulation of condensed-phase correlated electrons via trotterization,"Recent work has deployed linear combinations of unitaries techniques to reduce the cost of fault-tolerant quantum simulations of correlated electron models. Here, we show that one can sometimes improve upon those results with optimized implementations of Trotter-Suzuki-based product formulas. We show that low-order Trotter methods perform surprisingly well when used with phase estimation to compute relative precision quantities (e.g. energies per unit cell), as is often the goal for condensed-phase systems. In this context, simulations of the Hubbard and plane-wave electronic structure models with $N < 10^5$ fermionic modes can be performed with roughly $O(1)$ and $O(N^2)$ T complexities. We perform numerics revealing tradeoffs between the error and gate complexity of a Trotter step; e.g., we show that split-operator techniques have less Trotter error than popular alternatives. By compiling to surface code fault-tolerant gates and assuming error rates of one part per thousand, we show that one can error-correct quantum simulations of interesting, classically intractable instances with a few hundred thousand physical qubits.",https://arxiv.org/abs/1902.10673,"",,2024-02-06 22:07:51.586+00,2024-02-06 22:07:51.586+00,19,,2024-02-06 22:07:51.585+00,"","",208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Improved Fault-Tolerant Quantum Simulation of Condensed-Phase Correlated Electrons via Trotterization,740,Physical simulation
+1566,f,T-count,709999998,2024-02-06 21:56:24.305+00,2024-02-06 21:56:24.305+00,,19,1009,1058,2018-05-09,"",,,,,,,,1058,2024-02-06 21:53:56.408+00,2024-02-06 21:53:56.408+00,,19,739,208,739,Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity,encoding electronic spectra in quantum circuits with linear t complexity,"We construct quantum circuits which exactly encode the spectra of correlated electron models up to errors from rotation synthesis. By invoking these circuits as oracles within the recently introduced ""qubitization"" framework, one can use quantum phase estimation to sample states in the Hamiltonian eigenbasis with optimal query complexity $O(λ/ ε)$ where $λ$ is an absolute sum of Hamiltonian coefficients and $ε$ is target precision. For both the Hubbard model and electronic structure Hamiltonian in a second quantized basis diagonalizing the Coulomb operator, our circuits have T gate complexity $O({N + \log (1/ε}))$ where $N$ is number of orbitals in the basis. This enables sampling in the eigenbasis of electronic structure Hamiltonians with T complexity $O(N^3 /ε+ N^2 \log(1/ε)/ε)$. Compared to prior approaches, our algorithms are asymptotically more efficient in gate complexity and require fewer T gates near the classically intractable regime. Compiling to surface code fault-tolerant gates and assuming per gate error rates of one part in a thousand reveals that one can error correct phase estimation on interesting instances of these problems beyond the current capabilities of classical methods using only about a million superconducting qubits in a matter of hours.",https://arxiv.org/abs/1805.03662,,,2024-02-06 21:48:36.488+00,2024-02-06 21:50:53.434+00,19,,2024-02-06 21:50:53.434+00,,,208,Physical simulation,Physical simulation,"",2024-02-06 21:53:56.403+00,2024-02-06 21:53:56.403+00,19,63,f,Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity,739,Physical simulation
diff --git a/src/quantum_volume.csv b/src/quantum_volume.csv
deleted file mode 100644
index 3b69c40..0000000
--- a/src/quantum_volume.csv
+++ /dev/null
@@ -1,43 +0,0 @@
-key,name,submissionId,platformName,provider,methodName,metricName,metricValue,qubitCount,circuitDepth,tableDate,arXiv
-57,Validating quantum computers using randomized model circuits,42,ibmq-tokyo,ibmq,Heavy bit string sampling,Quantum volume,16,,,10/11/2019,1811.12926
-61,A quantum-classical cloud platform optimized for variational hybrid algorithms,44,Rigetti Aspen-4,rigetti,Rigetti Aspen-4,Quantum volume,8,,,5/31/2020
-60,Demonstration of the trapped-ion quantum-CCD computer architecture,43,Honeywell System Model H0,honeywell,Heavy bit string sampling,Quantum volume,64,,,6/20/2020,2003.01293
-59,Demonstration of quantum volume 64 on a superconducting quantum computing system,41,ibmq-montreal,ibmq,ibmq-montreal,Quantum volume,64,,,9/4/2020
-270,Achieving Quantum Volume 128 on the Honeywell Quantum Computer,182,Quantinuum System Model H1-1,quantinuum,Heavy bit string sampling,Quantum volume,128,,,9/29/2020
-271,Honeywell Sets New Record For Quantum Computing Performance,181,Quantinuum System Model H1-1,quantinuum,Heavy bit string sampling,Quantum volume,512,,,3/2/2021
-307,"Re-examining the quantum volume test: Ideal distributions, compiler optimizations, confidence intervals, and scalable resource estimations",46,Quantinuum System Model H1-1,quantinuum,Heavy bit string sampling,Quantum volume,1024,,,10/27/2021
-272,"Demonstrating Benefits of Quantum Upgradable Design Strategy: System Model H1-2 First to Prove 2,048 Quantum Volume",179,Quantinuum System Model H1-2,quantinuum,Heavy bit string sampling,Quantum volume,2048,,,12/17/2021
-173,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,ibmq-hanoi,ibmq,Quantum volume,Quantum volume,32,,,3/8/2022
-165,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,ibmq-lagos,ibmq,Quantum volume,Quantum volume,32,,,3/8/2022
-170,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,ibmq-washington,ibmq,Quantum volume,Quantum volume,16,,,3/8/2022
-164,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,ibmq-casablanca,ibmq,Quantum volume,Quantum volume,16,,,3/8/2022
-167,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,ibmq-sydney,ibmq,Quantum volume,Quantum volume,16,,,3/8/2022
-159,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,ibmq-quito,ibmq,Quantum volume,Quantum volume,16,,,3/8/2022
-168,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,ibmq-toronto,ibmq,Quantum volume,Quantum volume,16,,,3/8/2022
-171,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,ibmq-auckland,ibmq,Quantum volume,Quantum volume,16,,,3/8/2022
-174,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,ibmq-mumbai,ibmq,Quantum volume,Quantum volume,16,,,3/8/2022
-160,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,ibmq-jakarta,ibmq,Quantum volume,Quantum volume,8,,,3/8/2022
-163,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,ibmq-perth,ibmq,Quantum volume,Quantum volume,8,,,3/8/2022
-166,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,ibmq-guadalupe,ibmq,Quantum volume,Quantum volume,32,,,3/8/2022
-169,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,ibmq-brooklyn,ibmq,Quantum volume,Quantum volume,32,,,3/8/2022
-175,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,ibmq-montreal,ibmq,Quantum volume,Quantum volume,32,,,3/8/2022
-172,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,ibmq-cairo,ibmq,Quantum volume,Quantum volume,16,,,3/8/2022
-157,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,ibmq-lima,ibmq,Quantum volume,Quantum volume,8,,,3/8/2022
-162,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,ibmq-bogota,ibmq,Quantum volume,Quantum volume,8,,,3/8/2022
-158,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,ibmq-belem,ibmq,Quantum volume,Quantum volume,8,,,3/8/2022
-161,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,ibmq-manila,ibmq,Quantum volume,Quantum volume,4,,,3/8/2022
-265,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,Quantinuum System Model H1-2,quantinuum,Quantum volume,Quantum volume,512,,,3/18/2022
-266,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,ionq,ionq,Quantum volume,Quantum volume,8,,,3/18/2022
-279,Pushing quantum performance forward with our highest Quantum Volume yet,163,ibmq-prague,ibmq,Heavy bit string sampling,Quantum volume,256,,,4/6/2022
-235,Pushing quantum performance forward with our highest Quantum Volume yet,163,ibmq-prague,ibmq,,Quantum volume,256,,,4/6/2022
-273,Quantinuum Announces Quantum Volume 4096 Achievement,180,Quantinuum System Model H1-2,quantinuum,Heavy bit string sampling,Quantum volume,4096,,,4/11/2022
-1413,Experimental benchmarking of an automated deterministic error suppression workflow for quantum algorithms,271,,,Fire Opal,Quantum volume,32,,,9/14/2022
-343,Quantinuum Sets New Record with Highest Ever Quantum Volume,281,Quantinuum System Model H1-1,quantinuum,Heavy bit string sampling,Quantum volume,8192,,,9/27/2022
-464,Quantum-centric supercomputing: The next wave of computing | IBM Research Blog,357,,,Heavy bit string sampling,Quantum volume,512,,,11/9/2022
-672,AQT Quantum Volume Update,459,PINE,PINE,Quantum volume,Quantum volume,128,,,2/8/2023
-821,Quantinuum Sets Industry Record for Hardware Performance with New Quantum Volume Milestone,465,Quantinuum System Model H1-1,quantinuum,Heavy bit string sampling,Quantum volume,32768,,,2/23/2023
-1067,A Race Track Trapped-Ion Quantum Processor,561,Quantinuum H2,quantinuum,Heavy bit string sampling,Quantum volume,65536,32,,5/5/2023,2110.14808 
-1258,"Quantinuum H-Series quantum computer accelerates through 3 more performance records for quantum volume: 217, 218, and 219",642,Quantinuum System Model H1-1,quantinuum,Heavy bit string sampling,Quantum volume,524288,,,6/30/2023
-176,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,oqc-lucy,oqc-lucy,Quantum volume,Quantum volume,1,,,3/8/2022
-177,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,Rigetti Aspen-11,rigetti,Quantum volume,Quantum volume,1,,,3/8/2022
-178,Quantum Volume in Practice: What Users Can Expect from NISQ Devices,123,Rigetti Aspen M-1,rigetti,Quantum volume,Quantum volume,1,,,3/8/2022
\ No newline at end of file
diff --git a/src/views/Task.js b/src/views/Task.js
index c8320de..248b00a 100644
--- a/src/views/Task.js
+++ b/src/views/Task.js
@@ -11,7 +11,7 @@ import { library } from '@fortawesome/fontawesome-svg-core'
 import { faEdit } from '@fortawesome/free-solid-svg-icons'
 import { FontAwesomeIcon } from '@fortawesome/react-fontawesome'
 import CategoryScroll from '../components/CategoryScroll'
-import { parse } from 'json2csv'
+import { Parser } from '@json2csv/plainjs'
 import Commento from '../components/Commento'
 import SocialShareIcons from '../components/SocialShareIcons'
 import SubscribeButton from '../components/SubscribeButton'
@@ -20,7 +20,6 @@ import { sortByCounts } from '../components/SortFunctions'
 import { renderLatex } from '../components/RenderLatex'
 import TopSubmitters from '../components/TopSubmitters'
 import SubmissionScroll from '../components/SubmissionScroll'
-const SotaChart = React.lazy(() => import('../components/SotaChart'))
 const QuantumVolumeChart = React.lazy(() => import('../components/QuantumVolumeChart'))
 
 library.add(faEdit)
@@ -190,7 +189,8 @@ class Task extends React.Component {
   handleCsvExport () {
     const fields = Object.keys(this.state.resultsJson[0])
     const opts = { fields }
-    const csv = parse(this.state.resultsJson, opts)
+    const parser = new Parser(opts)
+    const csv = parser.parse(this.state.resultsJson)
 
     const element = document.createElement('a')
     element.setAttribute('href', 'data:text/csv;charset=utf-8,' + encodeURIComponent(csv))
@@ -229,18 +229,7 @@ class Task extends React.Component {
               </div>
               <br />
             </span>}
-          {!this.state.item.isHideChart && this.props.match.params.id !== '34' &&
-            <Suspense fallback={<div>Loading...</div>}>
-              <SotaChart
-                chartId='task-detail'
-                xLabel='Time'
-                taskId={this.props.match.params.id}
-                onLoadData={this.handleOnLoadData}
-                logBase='2'
-                onCsvExport={this.handleCsvExport}
-              />
-            </Suspense>}
-          {!this.state.item.isHideChart && this.props.match.params.id === '34' &&
+          {!this.state.item.isHideChart &&
             <Suspense fallback={<div>Loading...</div>}>
               <QuantumVolumeChart
                 chartId='task-detail'
diff --git a/src/views/Vqa.js b/src/views/Vqa.js
index 2913523..7d35fae 100644
--- a/src/views/Vqa.js
+++ b/src/views/Vqa.js
@@ -12,6 +12,7 @@ import SotaChart from '../components/SotaChart'
 import SubmissionScroll from '../components/SubmissionScroll'
 import { withRouter, Link } from 'react-router-dom'
 import { renderLatex } from '../components/RenderLatex'
+import QuantumVolumeChart from '../components/QuantumVolumeChart'
 
 class Vqa extends React.Component {
   constructor (props) {
@@ -111,12 +112,12 @@ class Vqa extends React.Component {
             <div className='col-md col'>
               {this.state.featured &&
                 <CategoryItemBox item={this.state.featured} isWide isPreview isLoggedIn={this.props.isLoggedIn} type='task' className='submission' />}
-              <SotaChart
+              <QuantumVolumeChart
                 chartId={0}
                 xLabel='Time'
                 taskId={119}
                 key={0}
-                isSubsetDisabled
+                isQubits
               />
               <hr />
             </div>