Sync v2 from dev (#1443)

**Title:**
Fix v2 builds

**Summary:**
This PR syncs changes from `demonstrations` with `demonstrations_v2` and
adds `pennylane_qisket` as a individual dependency in
`tutorial_error_mitigation`

Author: Alan-eMartin

demonstrations_v2/adjoint_diff_benchmarking/demo.py

@@ -22,23 +22,20 @@
 import timeit
 import matplotlib.pyplot as plt
 import pennylane as qml
-import jax
-
-jax.config.update("jax_platform_name", "cpu")
-jax.config.update('jax_enable_x64', True)
+import pennylane.numpy as pnp
 
 plt.style.use("bmh")
 
 n_samples = 5
 
 
 def get_time(qnode, params):
-    globals_dict = {'grad': jax.grad, 'circuit': qnode, 'params': params}
+    globals_dict = {"grad": qml.grad, "circuit": qnode, "params": params}
     return timeit.timeit("grad(circuit)(params)", globals=globals_dict, number=n_samples)
 
 
 def wires_scaling(n_wires, n_layers):
-    key = jax.random.PRNGKey(42)
+    rng = pnp.random.default_rng(12345)
 
     t_adjoint = []
     t_ps = []
@@ -58,7 +55,7 @@ def circuit(params, wires):
 
         # set up the parameters
         param_shape = qml.StronglyEntanglingLayers.shape(n_wires=i_wires, n_layers=n_layers)
-        params = jax.random.normal(key, param_shape)
+        params = rng.normal(size=pnp.prod(param_shape), requires_grad=True).reshape(param_shape)
 
         t_adjoint.append(get_time(circuit_adjoint, params))
         t_backprop.append(get_time(circuit_backprop, params))
@@ -68,10 +65,10 @@ def circuit(params, wires):
 
 
 def layers_scaling(n_wires, n_layers):
-    key = jax.random.PRNGKey(42)
+    rng = pnp.random.default_rng(12345)
 
     dev = qml.device("lightning.qubit", wires=n_wires)
-    dev_python = qml.device('default.qubit', wires=n_wires)
+    dev_python = qml.device("default.qubit", wires=n_wires)
 
     t_adjoint = []
     t_ps = []
@@ -88,7 +85,7 @@ def circuit(params):
     for i_layers in n_layers:
         # set up the parameters
         param_shape = qml.StronglyEntanglingLayers.shape(n_wires=n_wires, n_layers=i_layers)
-        params = jax.random.normal(key, param_shape)
+        params = rng.normal(size=pnp.prod(param_shape), requires_grad=True).reshape(param_shape)
 
         t_adjoint.append(get_time(circuit_adjoint, params))
         t_backprop.append(get_time(circuit_backprop, params))
@@ -110,9 +107,9 @@ def circuit(params):
     # Generating the graphic
     fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
 
-    ax1.plot(wires_list, adjoint_wires, '.-', label="adjoint")
-    ax1.plot(wires_list, ps_wires, '.-', label="parameter-shift")
-    ax1.plot(wires_list, backprop_wires, '.-', label="backprop")
+    ax1.plot(wires_list, adjoint_wires, ".-", label="adjoint")
+    ax1.plot(wires_list, ps_wires, ".-", label="parameter-shift")
+    ax1.plot(wires_list, backprop_wires, ".-", label="backprop")
 
     ax1.legend()
 
@@ -122,16 +119,17 @@ def circuit(params):
     ax1.set_yscale("log")
     ax1.set_title("Scaling with wires")
 
-    ax2.plot(layers_list, adjoint_layers, '.-', label="adjoint")
-    ax2.plot(layers_list, ps_layers, '.-', label="parameter-shift")
-    ax2.plot(layers_list, backprop_layers, '.-', label="backprop")
+    ax2.plot(layers_list, adjoint_layers, ".-", label="adjoint")
+    ax2.plot(layers_list, ps_layers, ".-", label="parameter-shift")
+    ax2.plot(layers_list, backprop_layers, ".-", label="backprop")
 
     ax2.legend()
 
     ax2.set_xlabel("Number of layers")
     ax2.set_xticks(layers_list)
-    ax2.set_ylabel("Time")
-    ax2.set_title("Scaling with Layers")
+    ax2.set_ylabel("Log Time")
+    ax2.set_yscale("log")
+    ax2.set_title("Scaling with layers")
 
     plt.savefig("scaling.png")
 

demonstrations_v2/adjoint_diff_benchmarking/metadata.json

@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2021-11-23T00:00:00+00:00",
-    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
+    "dateOfLastModification": "2025-07-11T00:00:00+00:00",
     "categories": [
         "Getting Started"
     ],

demonstrations_v2/ahs_aquila/demo.py

@@ -132,7 +132,7 @@
 
 The modification of the energy levels when atoms are in proximity gives rise to Rydberg blockade,
 where atoms that have been driven by a pulse that would, in isolation, leave them in the excited state
-instead remain in the ground state due to neighboring atoms being excited. The distance within which two 
+instead remain in the ground state due to neighboring atoms being excited. The distance within which two
 neighboring atoms are effectively prevented from both being excited is referred to as the *blockade radius* :math:`R_b.`
 
 This brings us to our discussion of the second part of the Hamiltonian: the drive term.
@@ -220,6 +220,16 @@
     device_arn="arn:aws:braket:us-east-1::device/qpu/quera/Aquila",
     wires=3,
 )
+##############################################################################
+# .. rst-class:: sphx-glr-script-out
+#
+#
+#  .. code-block:: none
+#
+#      pennylane/pennylane/__init__.py:201: PennyLaneDeprecationWarning: pennylane.QuantumFunctionError is no longer accessible at top-level
+#      and must be imported as pennylane.exceptions.QuantumFunctionError. Support for top-level access will be removed in v0.43.
+#        warnings.warn(
+#
 
 rydberg_simulator = qml.device("braket.local.ahs", wires=3)
 

demonstrations_v2/ahs_aquila/metadata.json

@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2023-05-16T00:00:00+00:00",
-    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
+    "dateOfLastModification": "2025-07-11T00:00:00+00:00",
     "categories": ["Quantum Hardware", "Devices and Performance", "Quantum Computing"],
     "tags": [],
     "previewImages": [

demonstrations_v2/getting_started_with_hybrid_jobs/demo.py

@@ -10,7 +10,7 @@
 
 In this tutorial, we'll walk through how to create your first hybrid quantum-classical algorithms on AWS.
 With a single-line-of-code, we'll see how to scale from PennyLane simulators on your laptop to running full-scale experiments on AWS that leverage both powerful classical compute and quantum devices.
-You'll gain understanding of the hybrid jobs queue, including QPU priority queuing, and learn how to scale classical resources for resource-intensive tasks. 
+You'll gain understanding of the hybrid jobs queue, including QPU priority queuing, and learn how to scale classical resources for resource-intensive tasks.
 We hope these tools will empower you to start experimenting today with hybrid quantum algorithms!
 
 .. figure:: /_static/demonstration_assets/getting_started_with_hybrid_jobs/socialthumbnail_large_getting_started_with_hybrid_jobs.png
@@ -40,8 +40,8 @@
 time, enabling you to keep track of costs, budget, or custom metrics such as training loss or
 expectation values.
 
-Importantly, on specific QPUs, running your algorithm in Hybrid Jobs benefits from `parametric compilation <https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs-parametric-compilation.html>`__. 
-This reduces the overhead associated with the computationally expensive compilation step by compiling a circuit only once and not for every iteration in your hybrid algorithm. 
+Importantly, on specific QPUs, running your algorithm in Hybrid Jobs benefits from `parametric compilation <https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs-parametric-compilation.html>`__.
+This reduces the overhead associated with the computationally expensive compilation step by compiling a circuit only once and not for every iteration in your hybrid algorithm.
 This dramatically reduces the total runtime for many variational algorithms.
 For long-running hybrid jobs, Braket automatically uses the updated calibration data from the hardware provider when compiling your circuit to ensure the highest quality results.
 
@@ -67,6 +67,15 @@
 
 device = qml.device("braket.local.qubit", wires=1)
 
+##############################################################################
+# .. rst-class:: sphx-glr-script-out
+#
+#  .. code-block:: none
+#
+#       pennylane/__init__.py:200: PennyLaneDeprecationWarning: pennylane.QuantumFunctionError is no longer accessible at top-level
+#       and must be imported as pennylane.exceptions.QuantumFunctionError. Support for top-level access will be removed in v0.43.
+#         warnings.warn(
+
 ######################################################################
 # Now we define a circuit with two rotation gates and measure the expectation value in the
 # :math:`Z`-basis.
@@ -246,6 +255,22 @@ def circuit(params):
 # Additionally, we can plot the metrics recorded during the algorithm. Below we show the expectation
 # value decreases with each iteration as expected.
 #
+# .. note::
+#
+#     ``job.metrics()`` will be an empty dictionary while the job is running, and likely also for
+#     a few minutes afterwards. To make sure that the below plot generates correctly, it is recommended
+#     to block execution until the metrics are correctly reported. This can be done by:
+#
+#     * using a ``while`` loop to manually block execution until the metrics are available.
+#     * using a Python debugger and setting a breakpoint before the line that uses ``job.metrics()``
+#       and blocking as needed.
+#
+#     We will use a ``while`` loop as shown below.
+#
+
+while len(job.metrics()) == 0:
+    pass
+
 
 import pandas as pd
 import matplotlib.pyplot as plt
@@ -304,8 +329,8 @@ def circuit(params):
 # .. note::
 #   AWS devices must be declared within the body of the decorated function.
 #
-# .. warning:: 
-#   The Rigetti device used in this demo, AspenM3, has been retired. For an updated list of available hardware through 
+# .. warning::
+#   The Rigetti device used in this demo, AspenM3, has been retired. For an updated list of available hardware through
 #   Amazon Braket, please consult the `supported regions and devices documentation <https://docs.aws.amazon.com/braket/latest/developerguide/braket-devices.html>`__. The general steps outlined below still hold regardless of the choice of device, though.
 #
 

demonstrations_v2/getting_started_with_hybrid_jobs/metadata.json

@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2023-10-16T00:00:00+00:00",
-    "dateOfLastModification": "2025-01-14T00:00:00+00:00",
+    "dateOfLastModification": "2025-07-11T00:00:00+00:00",
     "categories": [
         "Devices and Performance"
     ],

demonstrations_v2/how_to_catalyst_lightning_gpu/demo.py

@@ -107,7 +107,7 @@
 #
 #  .. code-block:: none
 #
-#    1.7712995142661776
+#    1.2454125296886156
 #
 # Lightning-GPU has feature parity with `the rest of Lightning state-vector simulators <https://docs.pennylane.ai/projects/lightning/en/stable>`__,
 # providing native support for many PennyLane's operations and measurement processes with
@@ -141,7 +141,7 @@
 #
 #  .. code-block:: none
 #
-#    [ 8.8817842e-16 ... -6.2915415e-01  0.0000000e+00]
+#    [ -1.1102230e-16 ... -7.2645772e-01  0.0000000e+00]
 #
 #
 # Note that in the above example, we didn't use ``method="adjoint"``.
@@ -194,7 +194,7 @@
 #    Step = 7
 #    Step = 8
 #    Step = 9
-#    [1.24479175e-01 ... 9.45755959e-01 4.64060426e-01]
+#    [0.947667 ... 3.093198 0.8401809 ]
 #
 #
 # We used `Optax <https://github.com/google-deepmind/optax>`__,

demonstrations_v2/how_to_catalyst_lightning_gpu/metadata.json

@@ -9,7 +9,7 @@
         }
     ],
     "dateOfPublication": "2025-02-21T10:00:00+00:00",
-    "dateOfLastModification": "2025-02-21T10:00:00+00:00",
+    "dateOfLastModification": "2025-07-11T10:00:00+00:00",
     "categories": [
         "Getting Started",
         "Quantum Chemistry",

demonstrations_v2/how_to_use_qiskit1_with_pennylane/demo.py

@@ -113,7 +113,7 @@ def circuit():
 #
 #   .. code-block:: none
 #
-#     {'0': tensor(474, requires_grad=True), '1': tensor(550, requires_grad=True)}
+#     {'0': 474, '1': 550}
 #
 
 ######################################################################
@@ -403,7 +403,7 @@ def cost(phis, theta):
 #   .. code-block:: none
 #
 #     Optimized parameters: phis = [3.12829384 3.12823583], theta = [3.1310224]
-#     Optimized cost function val: -2.999796472821245
+#     Optimized cost function value: -2.999796472821245
 #
 
 ######################################################################

demonstrations_v2/how_to_use_qiskit1_with_pennylane/metadata.json

@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2024-07-02T00:00:00+00:00",
-    "dateOfLastModification": "2025-01-08T00:00:00+00:00",
+    "dateOfLastModification": "2025-07-10T00:00:00+00:00",
     "categories": [
         "Quantum Computing",
         "How-to"