[DO NOT MERGE] Testing capture enabled in demos

_static/demonstration_assets/barren_gadgets/barren_gadgets.py

@@ -1,4 +1,5 @@
 import pennylane as qml
+qml.capture.enable()
 from pennylane import numpy as np
 
 def non_identity_obs(obs):

_static/demonstration_assets/barren_gadgets/layered_ansatz.py

@@ -1,4 +1,5 @@
 import pennylane as qml
+qml.capture.enable()
 from pennylane import numpy as np
 
 """ Based on the SimplifiedTwoDesign template from pennylane

demonstrations_v2/adjoint_diff_benchmarking/demo.py

@@ -19,6 +19,7 @@
 import timeit
 import matplotlib.pyplot as plt
 import pennylane as qml
+qml.capture.enable()
 import pennylane.numpy as pnp
 
 plt.style.use("bmh")

demonstrations_v2/ahs_aquila/demo.py

@@ -212,6 +212,7 @@
 """
 
 import pennylane as qml
+qml.capture.enable()
 
 aquila = qml.device(
     "braket.aws.ahs",

demonstrations_v2/braket-parallel-gradients/demo.py

@@ -100,6 +100,7 @@
 # SV1 can now be loaded with the standard PennyLane :func:`~.pennylane.device`:
 
 import pennylane as qml
+qml.capture.enable()
 from pennylane import numpy as np
 
 n_wires = 25

demonstrations_v2/covalent_cloud_gpu/demo.py

@@ -33,6 +33,7 @@
 import covalent_cloud as cc
 import matplotlib.pyplot as plt
 import pennylane as qml
+qml.capture.enable()
 from matplotlib.colors import ListedColormap
 from pennylane import numpy as np
 from sklearn.datasets import make_blobs
@@ -266,6 +267,7 @@ def run_qsvm(n_samples, test_size):
 #    import covalent_cloud as cc
 #    import matplotlib.pyplot as plt
 #    import pennylane as qml
+qml.capture.enable()
 #    from matplotlib.colors import ListedColormap
 #    from pennylane import numpy as np
 #    from sklearn.datasets import make_blobs

demonstrations_v2/ensemble_multi_qpu/demo.py

@@ -38,6 +38,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pennylane as qml
+qml.capture.enable()
 import sklearn.datasets
 import sklearn.decomposition
 import torch

demonstrations_v2/function_fitting_qsp/demo.py

@@ -183,6 +183,7 @@ def generate_many_sro(a_vals):
 
 
 import pennylane as qml
+qml.capture.enable()
 
 
 def QSP_circ(phi, W):

demonstrations_v2/gbs/demo.py

@@ -137,6 +137,7 @@
 
 # import PennyLane
 import pennylane as qml
+qml.capture.enable()
 
 ##############################################################################
 # We must define the unitary matrix we would like to embed in the circuit.

demonstrations_v2/getting_started_with_hybrid_jobs/demo.py

@@ -62,6 +62,7 @@
 #
 
 import pennylane as qml
+qml.capture.enable()
 from pennylane import numpy as np
 
 

demonstrations_v2/gqe_training/demo.py

@@ -124,6 +124,7 @@
 
 import numpy as np
 import pennylane as qml
+qml.capture.enable()
 
 def generate_molecule_data(molecules="H2"):
     datasets = qml.data.load("qchem", molname=molecules)

demonstrations_v2/how_to_catalyst_lightning_gpu/demo.py

@@ -73,6 +73,7 @@
 # .. code-block:: python
 #
 #     import pennylane as qml
+qml.capture.enable()
 #     import jax.numpy as jnp
 #
 #     import jax

demonstrations_v2/how_to_use_qiskit1_with_pennylane/demo.py

@@ -97,6 +97,7 @@
 #
 
 import pennylane as qml
+qml.capture.enable()
 
 dev = qml.device("qiskit.basicsim", wires=2)
 

demonstrations_v2/ibm_pennylane/demo.py

@@ -42,6 +42,7 @@
 # Currently, there are three devices available — ``Aer``, ``BasicSim`` and ``Remote`` — that can be initialized
 # as follows:
 import pennylane as qml
+qml.capture.enable()
 from qiskit_aer import AerSimulator
 
 qubits = 4
@@ -138,6 +139,7 @@
 from qiskit_ibm_runtime import QiskitRuntimeService
 
 import pennylane as qml
+qml.capture.enable()
 
 # Obtaining the Hamiltonian for H2 from PennyLane QChem dataset
 [dataset] = qml.data.load("qchem", molname="H2", bondlength=0.742, basis="STO-3G")

demonstrations_v2/learning2learn/demo.py

@@ -140,6 +140,7 @@
 
 # Quantum Machine Learning
 import pennylane as qml
+qml.capture.enable()
 from pennylane import qaoa
 
 # Classical Machine Learning

demonstrations_v2/ml_classical_shadows/demo.py

@@ -117,6 +117,7 @@ def build_coupling_mats(num_mats, num_rows, num_cols):
 #
 
 import pennylane as qml
+qml.capture.enable()
 
 def Hamiltonian(J_mat):
     coeffs, ops = [], []

demonstrations_v2/oqc_pulse/demo.py

@@ -80,6 +80,7 @@
 """
 
 import pennylane as qml
+qml.capture.enable()
 import numpy as np
 import jax.numpy as jnp
 import jax

demonstrations_v2/plugins_hybrid/demo.py

@@ -133,6 +133,7 @@
 """
 
 import pennylane as qml
+qml.capture.enable()
 from pennylane import numpy as np
 
 ##############################################################################

demonstrations_v2/pytorch_noise/demo.py

@@ -68,6 +68,7 @@
 """
 
 import pennylane as qml
+qml.capture.enable()
 import torch
 from torch.autograd import Variable
 
@@ -200,6 +201,7 @@ def cost(phi, theta, step):
 # :doc:`Computing gradients in parallel with Amazon Braket <demos/braket-parallel-gradients>`.
 
 import pennylane as qml
+qml.capture.enable()
 import torch
 from torch.autograd import Variable
 

demonstrations_v2/qnn_module_tf/demo.py

@@ -95,6 +95,7 @@
 # operations from the :doc:`templates <introduction/templates>` module.
 
 import pennylane as qml
+qml.capture.enable()
 
 n_qubits = 2
 dev = qml.device("default.qubit", wires=n_qubits)

demonstrations_v2/qnspsa/demo.py

@@ -187,6 +187,7 @@
 # initialize a graph for the max cut problem
 import networkx as nx
 import pennylane as qml
+qml.capture.enable()
 from pennylane import qaoa
 
 nodes = n_qubits = 4
@@ -636,6 +637,7 @@ def get_next_params(params, gradient):
 
 import random
 import pennylane as qml
+qml.capture.enable()
 from pennylane import numpy as np
 from scipy.linalg import sqrtm
 import warnings

demonstrations_v2/qonn/demo.py

@@ -77,6 +77,7 @@
 #
 
 import pennylane as qml
+qml.capture.enable()
 from pennylane import numpy as np
 
 import nlopt

demonstrations_v2/qrack/demo.py

@@ -95,6 +95,7 @@
 """
 
 import pennylane as qml
+qml.capture.enable()
 from pennylane import numpy as np
 from catalyst import qjit
 

demonstrations_v2/qsim_beyond_classical/demo.py

@@ -69,6 +69,7 @@
 #
 
 import pennylane as qml
+qml.capture.enable()
 from pennylane_cirq import ops
 
 import cirq

demonstrations_v2/quantum_neural_net/demo.py

@@ -36,6 +36,7 @@
 """
 
 import pennylane as qml
+qml.capture.enable()
 from pennylane import numpy as np
 from pennylane.optimize import AdamOptimizer
 

demonstrations_v2/quantum_volume/demo.py

@@ -330,6 +330,7 @@
 # :func:`~.pennylane.Permute` subroutine.
 
 import pennylane as qml
+qml.capture.enable()
 
 # Object for random number generation from numpy
 rng = np.random.default_rng()

demonstrations_v2/tutorial_How_to_optimize_QML_model_using_JAX_and_JAXopt/demo.py

@@ -31,6 +31,7 @@
 #
 
 import pennylane as qml
+qml.capture.enable()
 import jax
 from jax import numpy as jnp
 import jaxopt

demonstrations_v2/tutorial_How_to_optimize_QML_model_using_JAX_and_Optax/demo.py

@@ -32,6 +32,7 @@
 #
 
 import pennylane as qml
+qml.capture.enable()
 import jax
 from jax import numpy as jnp
 import optax

demonstrations_v2/tutorial_How_to_optimize_QML_model_using_JAX_catalyst_and_Optax/demo.py

@@ -31,6 +31,7 @@
 """
 
 import pennylane as qml
+qml.capture.enable()
 from jax import numpy as jnp
 import optax
 import catalyst

demonstrations_v2/tutorial_How_to_simulate_quantum_circuits_with_tensor_networks/demo.py

@@ -52,6 +52,7 @@
 #
 
 import pennylane as qml
+qml.capture.enable()
 import numpy as np
 
 # Define the keyword arguments for the MPS method
@@ -137,6 +138,7 @@ def circuit(theta, phi, num_qubits):
 #
 
 import pennylane as qml
+qml.capture.enable()
 import numpy as np
 
 # Define the keyword arguments for the TN method

demonstrations_v2/tutorial_QGAN/demo.py

@@ -51,6 +51,7 @@
 
 import numpy as np
 import pennylane as qml
+qml.capture.enable()
 import tensorflow as tf
 
 ##############################################################################

demonstrations_v2/tutorial_QUBO/demo.py

@@ -350,6 +350,7 @@ def time_to_solution(n, time_single_case):
 # -----------------------------   QAOA circuit ------------------------------------
 from collections import defaultdict
 import pennylane as qml
+qml.capture.enable()
 
 shots = 5000  # Number of samples used
 dev = qml.device("default.qubit")

demonstrations_v2/tutorial_adaptive_circuits/demo.py

@@ -59,6 +59,7 @@
 """
 
 import pennylane as qml
+qml.capture.enable()
 import jax
 import numpy as np
 import time

demonstrations_v2/tutorial_adjoint_diff/demo.py

@@ -70,6 +70,7 @@
 # To start, we import PennyLane and Jax's numpy:
 
 import pennylane as qml
+qml.capture.enable()
 import pennylane.numpy as pnp
 
 

demonstrations_v2/tutorial_adversarial_attacks_QML/demo.py

@@ -78,6 +78,7 @@
 #
 
 import pennylane as qml
+qml.capture.enable()
 from pennylane import numpy as np
 import torch
 from matplotlib import pyplot as plt

demonstrations_v2/tutorial_annni/demo.py

@@ -43,6 +43,7 @@
 as well as setting some important constants:
 """
 import pennylane as qml
+qml.capture.enable()
 import numpy as np
 from jax import jit, vmap, value_and_grad, random, config
 from jax import numpy as jnp

demonstrations_v2/tutorial_apply_qsvt/demo.py

@@ -36,6 +36,7 @@
 """
 
 import pennylane as qml
+qml.capture.enable()
 from pennylane import numpy as np
 
 dev = qml.device("lightning.qubit", wires=[0, 1])

demonstrations_v2/tutorial_backprop/demo.py

@@ -53,6 +53,7 @@
 Let's have a go implementing the parameter-shift rule manually in PennyLane.
 """
 import pennylane as qml
+qml.capture.enable()
 from jax import numpy as jnp
 from matplotlib import pyplot as plt
 import jax

demonstrations_v2/tutorial_barren_gadgets/barren_gadgets/barren_gadgets.py

@@ -1,4 +1,5 @@
 import pennylane as qml
+qml.capture.enable()
 from pennylane import numpy as np
 
 def non_identity_obs(obs):

demonstrations_v2/tutorial_barren_gadgets/barren_gadgets/layered_ansatz.py

@@ -1,4 +1,5 @@
 import pennylane as qml
+qml.capture.enable()
 from pennylane import numpy as np
 
 """ Based on the SimplifiedTwoDesign template from pennylane

demonstrations_v2/tutorial_barren_gadgets/demo.py

@@ -156,6 +156,7 @@
 # Finally, ``build_ansatz`` puts the pieces together. 
 
 import pennylane as qml
+qml.capture.enable()
 from pennylane import numpy as np
 from barren_gadgets.barren_gadgets import PerturbativeGadgets
 from barren_gadgets.layered_ansatz import (

demonstrations_v2/tutorial_barren_plateaus/demo.py

@@ -76,6 +76,7 @@
 """
 
 import pennylane as qml
+qml.capture.enable()
 from pennylane import numpy as np
 import matplotlib.pyplot as plt
 

demonstrations_v2/tutorial_block_encoding/demo.py

@@ -85,6 +85,7 @@
 """
 
 import pennylane as qml
+qml.capture.enable()
 from pennylane.templates.state_preparations.mottonen import compute_theta, gray_code
 import numpy as np
 import matplotlib.pyplot as plt

demonstrations_v2/tutorial_bluequbit/demo.py

@@ -38,6 +38,7 @@
 """
 
 import pennylane as qml
+qml.capture.enable()
 import matplotlib.pyplot as plt
 import numpy as np
 

demonstrations_v2/tutorial_bp_catalyst/demo.py

@@ -103,6 +103,7 @@
 from typing import Callable, Optional, Dict, Union, Sequence
 
 import pennylane as qml
+qml.capture.enable()
 
 syndromes = ["XXZIZIX", "XXIIZZI"]
 dev = qml.device("lightning.qubit", wires := max(map(len, syndromes)) + 1)
@@ -740,6 +741,7 @@ def ml_rep_decoder(syndrome: ArrayLike) -> ArrayLike:
 #
 
 import pennylane as qml
+qml.capture.enable()
 from jax import random
 import catalyst