Merge branch 'master' into contextuality_demo

.github/workflows/build-branch.yml

@@ -305,6 +305,9 @@ jobs:
           
           # Adding this install outside of poetry as the dependencies conflict with existing other packages.
           # TODO: This needs to be revisited.
+          # The following needs to be done in order for this dependency to be moved into pyproject.toml:
+          #  - PennyLane-Cirq needs to support cirq >= 1.1.0
+          #  - PennyLane needs to support numpy ~> 1.24
           ${{ steps.venv.outputs.location }}/bin/python3 -m pip install --no-deps mitiq==0.25.0
 
       - name: Download Worker Load Data Artifact
@@ -432,17 +435,6 @@ jobs:
           retention-days: 1
           path: /tmp/execution_times
 
-      # These are files that are generated as part of sphinx-build but are not needed and supported on the live website
-      # There does not seem to be an option to "not" generate them, therefore this step deletes these files before they
-      # are published to the live website.
-      - name: Update sitemap.xml
-        run: |
-          ${{ steps.venv.outputs.location }}/bin/qml_pipeline_utils \
-          clean-sitemap \
-          --build-dir="${{ github.workspace }}/_build/html" \
-          --html-files="demos/sg_execution_times.html" \
-          --verbose
-
       # Removes built html files that are not relevant to current node
       # See documentation in github_job_scheduler.py for more details.
       - name: Clean HTML Files

README.md

@@ -242,10 +242,12 @@ Add the new dependency in the `[tool.poetry.group.executable-dependencies.depend
 
 Once pyproject.toml files have been updated, the poetry.lock file needs to be refreshed:
 ```bash
-poetry lock
+poetry lock --no-update
 ```
 This command will ensure that there are no dependency conflicts with any other package, and everything works.
 
+The `--no-update` ensures existing package versions are not bumped as part of the locking process.
+
 If the dependency change is required in prod, open the PR against `master`, or if it's only required in dev, then open
 the PR against the `dev` branch, which will be synced to master on the next release of PennyLane.
 

conf.py

@@ -50,8 +50,7 @@
     "sphinx.ext.intersphinx",
     "sphinx.ext.mathjax",
     "sphinx.ext.ifconfig",
-    "sphinx_gallery.gen_gallery",
-    "sphinx_sitemap",
+    "sphinx_gallery.gen_gallery"
 ]
 
 

demonstrations/ahs_aquila.py

@@ -613,7 +613,7 @@ def circuit(params):
 start_val = amplitude[0]
 stop_val = amplitude[-1]
 max_val = np.max(amplitude)
-max_rate = np.max([(amplitude[i + 1] - amplitude[i]) / timestep for i in range(len(times))])
+max_rate = np.max([(amplitude[i + 1] - amplitude[i]) / timestep for i in range(len(times)-1)])
 
 print(f"start value: {start_val:.3} MHz")
 print(f"stop value: {stop_val:.3} MHz")

demonstrations/quantum_neural_net.py

@@ -84,7 +84,7 @@ def quantum_neural_net(var, x):
     for v in var:
         layer(v)
 
-    return qml.expval(qml.X(0))
+    return qml.expval(qml.QuadX(0))
 
 
 ##############################################################################

demonstrations/tutorial_QGAN.py

@@ -204,7 +204,7 @@ def gen_cost(gen_weights):
 # We begin by creating the optimizer:
 
 opt = tf.keras.optimizers.SGD(0.4)
-
+opt.build([disc_weights, gen_weights])
 
 ##############################################################################
 # In the first stage of training, we optimize the discriminator while
@@ -213,7 +213,7 @@ def gen_cost(gen_weights):
 cost = lambda: disc_cost(disc_weights)
 
 for step in range(50):
-    opt.minimize(cost, disc_weights)
+    opt.minimize(cost, [disc_weights])
     if step % 5 == 0:
         cost_val = cost().numpy()
         print("Step {}: cost = {}".format(step, cost_val))
@@ -243,7 +243,7 @@ def gen_cost(gen_weights):
 cost = lambda: gen_cost(gen_weights)
 
 for step in range(50):
-    opt.minimize(cost, gen_weights)
+    opt.minimize(cost, [gen_weights])
     if step % 5 == 0:
         cost_val = cost().numpy()
         print("Step {}: cost = {}".format(step, cost_val))

demonstrations/tutorial_apply_qsvt.py

@@ -341,7 +341,7 @@ def real_u(A, phi):
 
 @qml.qnode(qml.device("default.qubit", wires=["ancilla1", "ancilla2", 0, 1, 2]))
 def linear_system_solver_circuit(phi):
-    qml.QubitStateVector(normalized_b, wires=[1, 2])
+    qml.StatePrep(normalized_b, wires=[1, 2])
     real_u(A.T, phi)  # invert the singular values of A transpose to get A^-1
     return qml.state()
 

demonstrations/tutorial_expressivity_fourier_series.py

@@ -800,7 +800,7 @@ def quantum_model(weights, x):
     W(weights[0])
     S(x)
     W(weights[1])
-    return qml.expval(qml.X(wires=0))
+    return qml.expval(qml.QuadX(wires=0))
 
 def random_weights():
     return np.random.normal(size=(2, 5 * n_ansatz_layers), loc=0, scale=var)

demonstrations/tutorial_general_parshift.py

@@ -143,7 +143,7 @@ def make_cost(N, seed):
     @qml.qnode(dev, interface="jax")
     def cost(x):
         """Cost function on N qubits with N frequencies."""
-        qml.QubitStateVector(random_state(N, seed), wires=dev.wires)
+        qml.StatePrep(random_state(N, seed), wires=dev.wires)
         for w in dev.wires:
             qml.RZ(x, wires=w, id="x")
         return qml.expval(qml.Hermitian(random_observable(N, seed), wires=dev.wires))

demonstrations/tutorial_mbqc.py

@@ -162,7 +162,7 @@ def cluster_state():
 @qml.qnode(dev, interface="autograd")
 def one_bit_teleportation(input_state):
     # Prepare the input state
-    qml.QubitStateVector(input_state, wires=0)
+    qml.StatePrep(input_state, wires=0)
 
     # Prepare the cluster state
     qml.Hadamard(wires=1)
@@ -305,7 +305,7 @@ def generate_random_state(n=1):
 @qml.qnode(dev, interface="autograd")
 def RZ(theta, input_state):
     # Prepare the input state
-    qml.QubitStateVector(input_state, wires=0)
+    qml.StatePrep(input_state, wires=0)
 
     # Perform the Rz rotation
     qml.RZ(theta, wires=0)
@@ -326,7 +326,7 @@ def RZ(theta, input_state):
 @qml.qnode(mbqc_dev, interface="autograd")
 def RZ_MBQC(theta, input_state):
     # Prepare the input state
-    qml.QubitStateVector(input_state, wires=0)
+    qml.StatePrep(input_state, wires=0)
 
     # Prepare the cluster state
     qml.Hadamard(wires=1)
@@ -367,7 +367,7 @@ def RZ_MBQC(theta, input_state):
 @qml.qnode(dev, interface="autograd")
 def RX(theta, input_state):
     # Prepare the input state
-    qml.QubitStateVector(input_state, wires=0)
+    qml.StatePrep(input_state, wires=0)
 
     # Perform the Rz rotation
     qml.RX(theta, wires=0)
@@ -382,7 +382,7 @@ def RX(theta, input_state):
 @qml.qnode(mbqc_dev, interface="autograd")
 def RX_MBQC(theta, input_state):
     # Prepare the input state
-    qml.QubitStateVector(input_state, wires=0)
+    qml.StatePrep(input_state, wires=0)
 
     # Prepare the cluster state
     qml.Hadamard(wires=1)
@@ -447,7 +447,7 @@ def RX_MBQC(theta, input_state):
 @qml.qnode(dev, interface="autograd")
 def CNOT(input_state):
     # Prepare the input state
-    qml.QubitStateVector(input_state, wires=[0, 1])
+    qml.StatePrep(input_state, wires=[0, 1])
     qml.CNOT(wires=[0, 1])
 
     return qml.density_matrix(wires=[0, 1])
@@ -459,7 +459,7 @@ def CNOT(input_state):
 @qml.qnode(mbqc_dev, interface="autograd")
 def CNOT_MBQC(input_state):
     # Prepare the input state
-    qml.QubitStateVector(input_state, wires=[0, 1])
+    qml.StatePrep(input_state, wires=[0, 1])
 
     # Prepare the cluster state
     qml.Hadamard(wires=2)