Merge branch 'master' into mps-mcm-demo

PennyLaneAI · Oct 9, 2024 · 57988a5 · 57988a5
2 parents 132c922 + a87a0ae
commit 57988a5
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diff --git a/.github/workflows/validate-demo-metadata.yml b/.github/workflows/validate-demo-metadata.yml
@@ -101,7 +101,7 @@ jobs:
           METADATA_FILE_LIST: ${{ needs.generate-metadata-file-list.outputs.metadata_files }}
         run: |
           cd metadata_schemas
-          ${{ steps.poetry.outputs.bin }} run check-jsonschema -v --traceback-mode full --schemafile demo.metadata.schema.0.1.3.json $METADATA_FILE_LIST
+          ${{ steps.poetry.outputs.bin }} run check-jsonschema -v --traceback-mode full --schemafile demo.metadata.schema.0.1.4.json $METADATA_FILE_LIST
 
   validate-metadata-preview-images:
     runs-on: ubuntu-latest

diff --git a/demonstrations/adjoint_diff_benchmarking.metadata.json b/demonstrations/adjoint_diff_benchmarking.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2021-11-23T00:00:00+00:00",
-    "dateOfLastModification": "2024-08-06T00:00:00+00:00",
+    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
     "categories": [],
     "tags": [],
     "previewImages": [
@@ -17,7 +17,6 @@
     ],
     "seoDescription": "Benchmarking file for adjoint diff demonstration.",
     "doi": "",
-    "canonicalURL": "/qml/demos/adjoint_diff_benchmarking",
     "references": [],
     "basedOnPapers": [],
     "referencedByPapers": [],

diff --git a/demonstrations/ahs_aquila.metadata.json b/demonstrations/ahs_aquila.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2023-05-16T00:00:00+00:00",
-    "dateOfLastModification": "2024-09-04T00:00:00+00:00",
+    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
     "categories": ["Quantum Hardware", "Devices and Performance", "Quantum Computing"],
     "tags": [],
     "previewImages": [
@@ -17,7 +17,6 @@
     ],
     "seoDescription": "Perform measurements on neutral atom hardware through PennyLane",
     "doi": "",
-    "canonicalURL": "/qml/demos/ahs_aquila",
     "references": [
         {
             "id": "Semeghini",
@@ -76,4 +75,4 @@
             "logo": "/_static/hardware_logos/aws.png"
         }
     ]
-}
+}
diff --git a/demonstrations/braket-parallel-gradients.metadata.json b/demonstrations/braket-parallel-gradients.metadata.json
@@ -12,7 +12,7 @@
         }
     ],
     "dateOfPublication": "2020-12-08T00:00:00+00:00",
-    "dateOfLastModification": "2024-08-14T00:00:00+00:00",
+    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
     "categories": [
         "Devices and Performance"
     ],
@@ -25,7 +25,6 @@
     ],
     "seoDescription": "Parallelize gradient calculations with Amazon Braket",
     "doi": "",
-    "canonicalURL": "/qml/demos/braket-parallel-gradients",
     "references": [],
     "basedOnPapers": [],
     "referencedByPapers": [],
@@ -48,4 +47,4 @@
             "logo": "/_static/hardware_logos/aws.png"
         }
     ]
-}
+}
diff --git a/demonstrations/circuits_as_fourier_series.metadata.json b/demonstrations/circuits_as_fourier_series.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2023-09-11T00:00:00+00:00",
-    "dateOfLastModification": "2024-08-05T00:00:00+00:00",
+    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
     "categories": [
         "Optimization",
         "Quantum Machine Learning"
@@ -24,7 +24,6 @@
     ],
     "seoDescription": "Learn with this interactive, code-free introduction to the idea of quantum circuits as Fourier series.",
     "doi": "",
-    "canonicalURL": "/qml/demos/circuits_as_fourier_series",
     "references": [],
     "basedOnPapers": [],
     "referencedByPapers": [],

diff --git a/demonstrations/covalent_cloud_gpu.metadata.json b/demonstrations/covalent_cloud_gpu.metadata.json
@@ -9,7 +9,7 @@
         }
     ],
     "dateOfPublication": "2024-05-24T00:00:00+00:00",
-    "dateOfLastModification": "2024-08-05T00:00:00+00:00",
+    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
     "categories": [
         "Quantum Machine Learning"
     ],
@@ -26,7 +26,6 @@
     ],
     "seoDescription": "Explore the process of running GPU-accelerated quantum circuit simulations on Covalent Cloud using PennyLane.",
     "doi": "",
-    "canonicalURL": "/qml/demos/covalent_cloud_gpu",
     "references": [],
     "basedOnPapers": [],
     "referencedByPapers": [],

diff --git a/demonstrations/ensemble_multi_qpu.metadata.json b/demonstrations/ensemble_multi_qpu.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2020-02-14T00:00:00+00:00",
-    "dateOfLastModification": "2024-08-14T00:00:00+00:00",
+    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
     "categories": [
         "Quantum Machine Learning"
     ],
@@ -19,7 +19,6 @@
     ],
     "seoDescription": "We demonstrate how two QPUs can be combined in parallel to help solve a machine learning classification problem, using PyTorch and PennyLane.",
     "doi": "",
-    "canonicalURL": "/qml/demos/ensemble_multi_qpu",
     "references": [],
     "basedOnPapers": [],
     "referencedByPapers": [],

diff --git a/demonstrations/function_fitting_qsp.metadata.json b/demonstrations/function_fitting_qsp.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2022-05-24T00:00:00+00:00",
-    "dateOfLastModification": "2024-08-06T00:00:00+00:00",
+    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
     "categories": [
         "Quantum Machine Learning"
     ],
@@ -19,7 +19,6 @@
     ],
     "seoDescription": "Learn how to create polynomial approximations to functions using Quantum Signal Processing (QSP).",
     "doi": "",
-    "canonicalURL": "/qml/demos/function_fitting_qsp",
     "references": [
         {
             "id": "Martyn2021",

diff --git a/demonstrations/gbs.metadata.json b/demonstrations/gbs.metadata.json
@@ -9,7 +9,7 @@
         }
     ],
     "dateOfPublication": "2020-12-04T00:00:00+00:00",
-    "dateOfLastModification": "2024-08-06T00:00:00+00:00",
+    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
     "categories": [
         "Quantum Hardware",
         "Quantum Computing"
@@ -23,7 +23,6 @@
     ],
     "seoDescription": "Using light to perform tasks beyond the reach of classical computers.",
     "doi": "",
-    "canonicalURL": "/qml/demos/gbs",
     "references": [
         {
             "id": "Arute2019",
@@ -98,4 +97,4 @@
             "weight": 1.0
         }
     ]
-}
+}
diff --git a/demonstrations/getting_started_with_hybrid_jobs.metadata.json b/demonstrations/getting_started_with_hybrid_jobs.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2023-10-16T00:00:00+00:00",
-    "dateOfLastModification": "2024-08-05T00:00:00+00:00",
+    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
     "categories": [
         "Devices and Performance"
     ],
@@ -23,7 +23,6 @@
     ],
     "seoDescription": "Getting started with the Amazon Braket Hybrid Jobs",
     "doi": "",
-    "canonicalURL": "/qml/demos/getting_started_with_hybrid_jobs",
     "references": [],
     "basedOnPapers": [],
     "referencedByPapers": [],
@@ -35,4 +34,4 @@
             "logo": "/_static/hardware_logos/aws.png"
         }
     ]
-}
+}
diff --git a/demonstrations/gqe_training.metadata.json b/demonstrations/gqe_training.metadata.json
@@ -3,13 +3,13 @@
     "authors": [
         {
             "username": "Joseph"
-        }, 
+        },
         {
             "username": "zy_n"
         }
     ],
     "dateOfPublication": "2024-09-20T00:00:00+00:00",
-    "dateOfLastModification": "2024-09-20T00:00:00+00:00",
+    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
     "categories": ["Quantum Machine Learning", "Quantum Chemistry", "Algorithms"],
     "tags": [],
     "previewImages": [
@@ -24,7 +24,6 @@
     ],
     "seoDescription": "Learn how you can train a small GPT model using the generative quantum eigensolver (GQE) technique and PennyLane data.",
     "doi": "",
-    "canonicalURL": "/qml/demos/gqe_training",
     "references": [
         {
             "id": "nakaji2024",
@@ -71,4 +70,4 @@
         }
     ],
     "hardware": []
-}
+}
diff --git a/demonstrations/how_to_use_qiskit1_with_pennylane.metadata.json b/demonstrations/how_to_use_qiskit1_with_pennylane.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2024-07-02T00:00:00+00:00",
-    "dateOfLastModification": "2024-08-06T00:00:00+00:00",
+    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
     "categories": [
         "Quantum Computing",
         "How-to"
@@ -24,7 +24,6 @@
     ],
     "seoDescription": "Learn how to get started using Qiskit 1.0 with PennyLane",
     "doi": "",
-    "canonicalURL": "/qml/demos/how_to_use_qiskit1_with_pennylane",
     "references": [],
     "basedOnPapers": [],
     "referencedByPapers": [],

diff --git a/demonstrations/ibm_pennylane.metadata.json b/demonstrations/ibm_pennylane.metadata.json
@@ -12,7 +12,7 @@
         }
     ],
     "dateOfPublication": "2023-06-20T00:00:00+00:00",
-    "dateOfLastModification": "2024-08-08T00:00:00+00:00",
+    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
     "categories": [
         "Devices and Performance"
     ],
@@ -29,7 +29,6 @@
     ],
     "seoDescription": "Learn how to use IBM devices with PennyLane.",
     "doi": "",
-    "canonicalURL": "/qml/demos/ibm_pennylane",
     "references": [],
     "basedOnPapers": [],
     "referencedByPapers": [],

diff --git a/demonstrations/learning2learn.metadata.json b/demonstrations/learning2learn.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2021-03-02T00:00:00+00:00",
-    "dateOfLastModification": "2024-09-04T00:00:00+00:00",
+    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
     "categories": [
         "Quantum Machine Learning"
     ],
@@ -19,7 +19,6 @@
     ],
     "seoDescription": "Use a classical recurrent neural network to initilize the parameters of a variational quatum algorithm.",
     "doi": "",
-    "canonicalURL": "/qml/demos/learning2learn",
     "references": [],
     "basedOnPapers": [
         "10.48550/arXiv.1907.05415"
@@ -37,4 +36,4 @@
             "weight": 1.0
         }
     ]
-}
+}
diff --git a/demonstrations/ml_classical_shadows.metadata.json b/demonstrations/ml_classical_shadows.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2022-05-02T00:00:00+00:00",
-    "dateOfLastModification": "2024-09-05T00:00:00+00:00",
+    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
     "categories": [
         "Quantum Machine Learning"
     ],
@@ -23,7 +23,6 @@
     ],
     "seoDescription": "Learn how to apply machine learning to quantum many-body problems by using classical shadow formalism and ML to predict the ground-state properties of the 2D antiferromagnetic Heisenberg model.",
     "doi": "",
-    "canonicalURL": "/qml/demos/ml_classical_shadows",
     "references": [
         {
             "id": "preskill",

diff --git a/demonstrations/oqc_pulse.metadata.json b/demonstrations/oqc_pulse.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2023-10-30T00:00:00+00:00",
-    "dateOfLastModification": "2024-08-06T00:00:00+00:00",
+    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
     "categories": [
         "Quantum Computing"
     ],
@@ -23,7 +23,6 @@
     ],
     "seoDescription": "Learn how to create polynomial approximations to functions using Quantum Signal Processing (QSP).",
     "doi": "",
-    "canonicalURL": "/qml/demos/oqc_pulse",
     "references": [
         {
             "id": "Rahamim",
@@ -71,4 +70,4 @@
             "weight": 1.0
         }
     ]
-}
+}
diff --git a/demonstrations/plugins_hybrid.metadata.json b/demonstrations/plugins_hybrid.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2019-10-11T00:00:00+00:00",
-    "dateOfLastModification": "2024-08-06T00:00:00+00:00",
+    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
     "categories": [
         "Devices and Performance"
     ],
@@ -19,7 +19,6 @@
     ],
     "seoDescription": "This tutorial introduces the notion of hybrid computation by combining several PennyLane device backends to train an algorithm containing both photonic and qubit devices.",
     "doi": "",
-    "canonicalURL": "/qml/demos/plugins_hybrid",
     "references": [],
     "basedOnPapers": [],
     "referencedByPapers": [],
@@ -35,4 +34,4 @@
             "weight": 1.0
         }
     ]
-}
+}
diff --git a/demonstrations/pytorch_noise.metadata.json b/demonstrations/pytorch_noise.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2019-10-11T00:00:00+00:00",
-    "dateOfLastModification": "2024-08-06T00:00:00+00:00",
+    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
     "categories": [
         "Devices and Performance"
     ],
@@ -19,7 +19,6 @@
     ],
     "seoDescription": "Extend PyTorch with real quantum computing power, by using it to optimize a noisy quantum hardware device.",
     "doi": "",
-    "canonicalURL": "/qml/demos/pytorch_noise",
     "references": [],
     "basedOnPapers": [],
     "referencedByPapers": [],

diff --git a/demonstrations/qnspsa.metadata.json b/demonstrations/qnspsa.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2022-07-18T00:00:00+00:00",
-    "dateOfLastModification": "2024-08-05T00:00:00+00:00",
+    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
     "categories": [
         "Optimization"
     ],
@@ -19,7 +19,6 @@
     ],
     "seoDescription": "Introduction to the Quantum natural SPSA optimizer, which reduces the number of quantum measurements in the optimization.",
     "doi": "",
-    "canonicalURL": "/qml/demos/qnspsa",
     "references": [
         {
             "id": "Gacon2021",
@@ -90,4 +89,4 @@
             "logo": "/_static/hardware_logos/aws.png"
         }
     ]
-}
+}