Replacement of legacy devices with default.qubit (#1176)

**Summary:** The legacy qubit devices `default.qubit.{tf,torch,jax,autograd,legacy}` are now deprecated and need to be replaced with `default.qubit`. [[sc-70260](https://app.shortcut.com/xanaduai/story/70260)]
PennyLaneAI · Aug 6, 2024 · 951ac78 · 951ac78
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diff --git a/demonstrations/ahs_aquila.metadata.json b/demonstrations/ahs_aquila.metadata.json
@@ -6,64 +6,68 @@
         }
     ],
     "dateOfPublication": "2023-05-16T00:00:00+00:00",
-    "dateOfLastModification": "2024-07-10T00:00:00+00:00",
-    "categories": ["Quantum Hardware", "Devices and Performance", "Quantum Computing"],
+    "dateOfLastModification": "2024-07-31T00:00:00+00:00",
+    "categories": [
+        "Quantum Hardware",
+        "Devices and Performance",
+        "Quantum Computing"
+    ],
     "tags": [],
     "previewImages": [
-         {
-             "type": "thumbnail",
-             "uri": "/_static/demonstration_assets/ahs_aquila/thumbnail_tutorial_pulse_on_hardware.png"
-         }
-     ],
+        {
+            "type": "thumbnail",
+            "uri": "/_static/demonstration_assets/ahs_aquila/thumbnail_tutorial_pulse_on_hardware.png"
+        }
+    ],
     "seoDescription": "Perform measurements on neutral atom hardware through PennyLane",
     "doi": "",
     "canonicalURL": "/qml/demos/ahs_aquila",
     "references": [
-         {
+        {
             "id": "Semeghini",
             "type": "article",
             "title": "Probing topological spin liquids on a programmable quantum simulator",
             "authors": "G. Semeghini, H. Levine, A. Keesling, S. Ebadi, T.T. Wang, D. Bluvstein, R. Verresen, H. Pichler, M. Kalinowski, R. Samajdar, A. Omran, S. Sachdev, A. Vishwanath, M. Greiner, V. Vuletic, M.D. Lukin",
             "year": "2021",
             "journal": "",
             "url": "https://arxiv.org/abs/2104.04119"
-         },
-         {
+        },
+        {
             "id": "Lienhard",
             "type": "article",
             "title": "Observing the Space- and Time-Dependent Growth of Correlations in Dynamically Tuned Synthetic Ising Models with Antiferromagnetic Interactions",
             "authors": "V. Lienhard, S. de Léséleuc, D. Barredo, T. Lahaye, A. Browaeys, M. Schuler, L.-P. Henry, A.M. Läuchli",
             "year": "2018",
             "journal": "",
             "url": "https://arxiv.org/abs/1711.01185"
-         },
-         {
+        },
+        {
             "id": "BraketDevGuide",
             "type": "webpage",
             "title": "Hello AHS: Run your first Analog Hamiltonian Simulation",
             "authors": "Amazon Web Services: Amazon Braket",
             "journal": "",
             "url": "https://docs.aws.amazon.com/braket/latest/developerguide/braket-get-started-hello-ahs.html"
-         },
-         {
+        },
+        {
             "id": "Asthana2022",
             "type": "article",
             "title": "AWS Quantum Technologies Blog: Realizing quantum spin liquid phase on an analog Hamiltonian Rydberg simulator",
             "authors": "Alexander Keesling, Eric Kessler, and Peter Komar",
             "year": "2021",
             "journal": "",
             "url": "https://aws.amazon.com/blogs/quantum-computing/realizing-quantum-spin-liquid-phase-on-an-analog-hamiltonian-rydberg-simulator/"
-         }
-     ],
+        }
+    ],
     "basedOnPapers": [],
     "referencedByPapers": [],
     "relatedContent": [
-         {
+        {
             "type": "demonstration",
             "id": "tutorial_pasqal",
             "weight": 1.0
         },
-         {
+        {
             "type": "demonstration",
             "id": "tutorial_pulse_programming101",
             "weight": 1.0
@@ -76,4 +80,4 @@
             "logo": "/_static/hardware_logos/aws.png"
         }
     ]
- }
+}
diff --git a/demonstrations/ahs_aquila.py b/demonstrations/ahs_aquila.py
@@ -512,7 +512,7 @@ def gaussian_fn(p, t):
 params = [amplitude_params]
 ts = [0.0, 1.75]
 
-default_qubit = qml.device("default.qubit.jax", wires=3, shots=1000)
+default_qubit = qml.device("default.qubit", wires=3, shots=1000)
 
 
 @qml.qnode(default_qubit, interface="jax")

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": "2024-07-03T00:00:00+00:00",
+    "dateOfLastModification": "2024-07-31T00:00:00+00:00",
     "categories": [
         "Quantum Machine Learning"
     ],
@@ -21,7 +21,9 @@
     "doi": "",
     "canonicalURL": "/qml/demos/learning2learn",
     "references": [],
-    "basedOnPapers": ["10.48550/arXiv.1907.05415"],
+    "basedOnPapers": [
+        "10.48550/arXiv.1907.05415"
+    ],
     "referencedByPapers": [],
     "relatedContent": [
         {
@@ -35,4 +37,4 @@
             "weight": 1.0
         }
     ]
-}
+}
diff --git a/demonstrations/learning2learn.py b/demonstrations/learning2learn.py
@@ -239,8 +239,7 @@ def circuit(params, **kwargs):
     def hamiltonian(params, **kwargs):
         """Evaluate the cost Hamiltonian, given the angles and the graph."""
 
-        # We set the default.qubit.tf device for seamless integration with TensorFlow
-        dev = qml.device("default.qubit.tf", wires=len(graph.nodes))
+        dev = qml.device("default.qubit", wires=len(graph.nodes))
 
         # This qnode evaluates the expectation value of the cost hamiltonian operator
         cost = qml.QNode(circuit, dev, diff_method="backprop", interface="tf")
@@ -373,9 +372,7 @@ def recurrent_loop(graph_cost, n_layers=1, intermediate_steps=False):
     # We perform five consecutive calls to 'rnn_iteration', thus creating the
     # recurrent loop. More iterations lead to better results, at the cost of
     # more computationally intensive simulations.
-    out0 = rnn_iteration(
-        [initial_cost, initial_params, initial_h, initial_c], graph_cost
-    )
+    out0 = rnn_iteration([initial_cost, initial_params, initial_h, initial_c], graph_cost)
     out1 = rnn_iteration(out0, graph_cost)
     out2 = rnn_iteration(out1, graph_cost)
     out3 = rnn_iteration(out2, graph_cost)
@@ -1030,9 +1027,7 @@ def call(self, inputs):
         _params = tf.reshape(new_params, shape=(2, self.qaoa_p))
 
         # Cost evaluation, and reshaping to be consistent with other Keras tensors
-        new_cost = tf.reshape(
-            tf.cast(self.expectation(_params), dtype=tf.float32), shape=(1, 1)
-        )
+        new_cost = tf.reshape(tf.cast(self.expectation(_params), dtype=tf.float32), shape=(1, 1))
 
         return [new_cost, new_params, new_h, new_c]
 

diff --git a/demonstrations/tutorial_eqnn_force_field.metadata.json b/demonstrations/tutorial_eqnn_force_field.metadata.json
@@ -3,15 +3,16 @@
     "authors": [
         {
             "id": "oriel_kiss"
-          },
-          {
+        },
+        {
             "id": "isabel_nha_minh_le"
         }
     ],
     "dateOfPublication": "2024-03-12T00:00:00+00:00",
-    "dateOfLastModification": "2024-03-13T00:00:00+00:00",
+    "dateOfLastModification": "2024-07-31T00:00:00+00:00",
     "categories": [
-        "Quantum Machine Learning", "Quantum Chemistry"
+        "Quantum Machine Learning",
+        "Quantum Chemistry"
     ],
     "tags": [],
     "previewImages": [
@@ -57,7 +58,7 @@
             "publisher": "APS",
             "journal": "Phys. Rev. A",
             "url": "https://journals.aps.org/pra/abstract/10.1103/PhysRevA.103.032430"
-        } ,
+        },
         {
             "id": "wierichs",
             "type": "article",
@@ -67,7 +68,7 @@
             "publisher": "",
             "journal": "",
             "url": "https://arxiv.org/abs/2312.06752"
-        } ,
+        },
         {
             "id": "meyer",
             "type": "article",
@@ -79,10 +80,11 @@
             "url": "https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.4.010328"
         }
     ],
-    "basedOnPapers": ["https://arxiv.org/abs/2311.11362"],
+    "basedOnPapers": [
+        "https://arxiv.org/abs/2311.11362"
+    ],
     "referencedByPapers": [],
     "relatedContent": [
-
         {
             "type": "demonstration",
             "id": "tutorial_geometric_qml",
@@ -104,4 +106,4 @@
             "weight": 1.0
         }
     ]
-}
+}
diff --git a/demonstrations/tutorial_eqnn_force_field.py b/demonstrations/tutorial_eqnn_force_field.py
@@ -133,12 +133,14 @@
 import numpy as np
 
 import jax
-jax.config.update('jax_platform_name', 'cpu')
+
+jax.config.update("jax_platform_name", "cpu")
 from jax import numpy as jnp
 
 import scipy
 import matplotlib.pyplot as plt
 import sklearn
+
 ######################################################################
 # Let us construct Pauli matrices, which are used to build the Hamiltonian.
 X = np.array([[0, 1], [1, 0]])
@@ -148,7 +150,11 @@
 sigmas = jnp.array(np.array([X, Y, Z]))  # Vector of Pauli matrices
 sigmas_sigmas = jnp.array(
     np.array(
-        [np.kron(X, X), np.kron(Y, Y), np.kron(Z, Z)]  # Vector of tensor products of Pauli matrices
+        [
+            np.kron(X, X),
+            np.kron(Y, Y),
+            np.kron(Z, Z),
+        ]  # Vector of tensor products of Pauli matrices
     )
 )
 
@@ -169,7 +175,6 @@ def singlet(wires):
     qml.CNOT(wires=wires)
 
 
-
 ######################################################################
 # Next, we need a rotationally equivariant data embedding. We choose to encode a three-dimensional
 # data point :math:`\vec{x}\in \mathbb{R}^3` via
@@ -201,7 +206,6 @@ def equivariant_encoding(alpha, data, wires):
     qml.QubitUnitary(U, wires=wires, id="E")
 
 
-
 ######################################################################
 # Finally, we require an equivariant trainable map and an invariant observable. We take the Heisenberg
 # Hamiltonian, which is rotationally invariant, as an inspiration. We define a single summand of it,
@@ -290,12 +294,12 @@ def noise_layer(epsilon, wires):
 rep = 2  # Number of repeated vertical encoding
 
 active_atoms = 2  # Number of active atoms
-                  # Here we only have two active atoms since we fixed the oxygen (which becomes non-active) at the origin
+# Here we only have two active atoms since we fixed the oxygen (which becomes non-active) at the origin
 num_qubits = active_atoms * rep
 #################################
 
 
-dev = qml.device("default.qubit.jax", wires=num_qubits)
+dev = qml.device("default.qubit", wires=num_qubits)
 
 
 @qml.qnode(dev, interface="jax")
@@ -311,9 +315,7 @@ def vqlm(data, params):
 
     # Initial encoding
     for i in range(num_qubits):
-        equivariant_encoding(
-            alphas[i, 0], jnp.asarray(data)[i % active_atoms, ...], wires=[i]
-        )
+        equivariant_encoding(alphas[i, 0], jnp.asarray(data)[i % active_atoms, ...], wires=[i])
 
     # Reuploading model
     for d in range(D):
@@ -437,6 +439,7 @@ def inference(loss_data, opt_state):
 
     return E_pred, l
 
+
 #################################
 # **Parameter initialization:**
 #
@@ -466,8 +469,8 @@ def inference(loss_data, opt_state):
 # We train our VQLM using stochastic gradient descent.
 
 
-num_batches = 5000 # number of optimization steps
-batch_size = 256   # number of training data per batch
+num_batches = 5000  # number of optimization steps
+batch_size = 256  # number of training data per batch
 
 
 for ibatch in range(num_batches):
@@ -492,7 +495,7 @@ def inference(loss_data, opt_state):
 history_loss = np.array(running_loss)
 
 fontsize = 12
-plt.figure(figsize=(4,4))
+plt.figure(figsize=(4, 4))
 plt.plot(history_loss[:, 0], "r-", label="training error")
 plt.plot(history_loss[:, 1], "b-", label="testing error")
 
@@ -512,7 +515,7 @@ def inference(loss_data, opt_state):
 # could be improved, e.g. by using a deeper model as in the original paper.
 #
 
-plt.figure(figsize=(4,4))
+plt.figure(figsize=(4, 4))
 plt.title("Energy predictions", fontsize=fontsize)
 plt.plot(energy[indices_test], E_pred, "ro", label="Test predictions")
 plt.plot(energy[indices_test], energy[indices_test], "k.-", lw=1, label="Exact")

diff --git a/demonstrations/tutorial_geometric_qml.metadata.json b/demonstrations/tutorial_geometric_qml.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2022-10-18T00:00:00+00:00",
-    "dateOfLastModification": "2024-01-01T00:00:00+00:00",
+    "dateOfLastModification": "2024-07-31T00:00:00+00:00",
     "categories": [
         "Quantum Machine Learning"
     ],
@@ -58,4 +58,4 @@
             "weight": 1.0
         }
     ]
-}
+}