merge dev into master for v0.42.0 release

Makefile

@@ -62,6 +62,7 @@ download:
 	wget --no-verbose -N https://download.pytorch.org/tutorial/hymenoptera_data.zip -P $(DATADIR)
 	unzip -q -o $(DATADIR)/hymenoptera_data.zip -d $(DATADIR)/
 
+# TODO: Update flax, or certain tutorials e.g. tutorial_rl_pulse.py will fail due to implicit confliction between jax==0.6.0 and flax==0.9.0
 environment:
 	@command -v $(POETRY_BIN) --version >/dev/null 2>&1 || { echo >&2 "Setting up the environment requires a valid installation of python-poetry. Please install and add poetry to PATH or pass the executable using POETRY_BIN. Aborting."; exit 1; }
 	@if [ '$(BASE_ONLY)' = 'true' ]; then\

README.md

@@ -165,7 +165,10 @@ Due to the large scope of requirements in this repository, the traditional `requ
 and `pyproject.toml` is being introduced instead, the goal being easier management in regard to adding/updating packages.
 
 To install all the dependencies locally, [poetry](https://python-poetry.org/) needs to be installed. Please follow the
-[official installation documentation](https://python-poetry.org/docs/#installation).
+[official installation documentation](https://python-poetry.org/docs/#installation). To ensure that no issues arise, we
+recommend using the [official installer](https://python-poetry.org/docs/#installing-with-the-official-installer).
+
+**IMPORTANT NOTE:** It is important to install Poetry using the official instructions and NOT into the same virtual env as QML itself, this would cause poetry to manage itself and raise unexpected errors.
 
 ### Installing dependencies
 

conf.py

@@ -18,12 +18,17 @@
 import numpy as np
 from jinja2 import FileSystemLoader, Environment
 import yaml
-from pennylane import PennyLaneDeprecationWarning
 from pathlib import Path
 
 sys.path.insert(0, os.path.abspath("."))
 sys.path.insert(0, os.path.join(os.path.dirname(__file__)))
 
+# This module has been refactored from pennylane v0.41.0 to v0.42.0
+# This can be removed after the release of v0.42.0
+try:
+    from pennylane.exceptions import PennyLaneDeprecationWarning
+except ModuleNotFoundError:
+    from pennylane import PennyLaneDeprecationWarning
 
 # -- Project information -----------------------------------------------------
 # General information about the project.

demonstrations/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/adjoint_diff_benchmarking.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/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": [