Merge branch 'master' into dev

PennyLaneAI · Sep 16, 2024 · f3831d0 · f3831d0
2 parents 7ffeaf3 + 6cb8957
commit f3831d0
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Showing 6 changed files with 37 additions and 25 deletions.
diff --git a/demonstrations/tutorial_eqnn_force_field.metadata.json b/demonstrations/tutorial_eqnn_force_field.metadata.json
@@ -9,7 +9,7 @@
         }
     ],
     "dateOfPublication": "2024-03-12T00:00:00+00:00",
-    "dateOfLastModification": "2024-08-30T00:00:00+00:00",
+    "dateOfLastModification": "2024-09-16T00:00:00+00:00",
     "categories": [
         "Quantum Machine Learning",
         "Quantum Chemistry"

diff --git a/demonstrations/tutorial_eqnn_force_field.py b/demonstrations/tutorial_eqnn_force_field.py
@@ -317,7 +317,9 @@ 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, dtype=complex)[i % active_atoms, ...], wires=[i]
+        )
 
     # Reuploading model
     for d in range(D):
@@ -339,7 +341,9 @@ def vqlm(data, params):
         # Encoding
         for i in range(num_qubits):
             equivariant_encoding(
-                alphas[i, d + 1], jnp.asarray(data)[i % active_atoms, ...], wires=[i]
+                alphas[i, d + 1],
+                jnp.asarray(data, dtype=complex)[i % active_atoms, ...],
+                wires=[i],
             )
 
     return qml.expval(Observable)

diff --git a/demonstrations/tutorial_vqe_vqd.metadata.json b/demonstrations/tutorial_vqe_vqd.metadata.json
@@ -9,7 +9,7 @@
         }
     ],
     "dateOfPublication": "2024-08-26T00:00:00+00:00",
-    "dateOfLastModification": "2024-08-28T00:00:00+00:00",
+    "dateOfLastModification": "2024-09-16T00:00:00+00:00",
     "categories": [
         "Quantum Chemistry",
         "How-to"

diff --git a/demonstrations/tutorial_vqe_vqd.py b/demonstrations/tutorial_vqe_vqd.py
@@ -45,9 +45,17 @@
 # To implement the VQD algorithm, we first need to know the ground state of our system, and it is a breeze to use the data from `PennyLane Datasets <https://pennylane.ai/datasets/>`__  to obtain the Hamiltonian and the ground state
 # of the hydrogen molecule:
 #
+# .. note::
+#
+#     To improve viewability of this tutorial, we will suppress any ``ComplexWarning``'s which may be raised during optimization.
+#     The warnings do not impact the correctness of the results, but make it harder to view outputs.
+#
 
 import pennylane as qml
-from pennylane import numpy as np
+import numpy as np
+
+import warnings
+warnings.filterwarnings(action="ignore", category=np.ComplexWarning)
 
 # Load the dataset
 h2 = qml.data.load("qchem", molname="H2", bondlength=0.742, basis="STO-3G")[0]
@@ -58,7 +66,7 @@
 
 # Obtain the ground state from the operations given by the dataset
 def generate_ground_state(wires):
-    qml.BasisState(h2.hf_state, wires=wires)
+    qml.BasisState(np.array(h2.hf_state), wires=wires)
 
     for op in h2.vqe_gates:  # use the gates data from the dataset
         op = qml.map_wires(op, {op.wires[i]: wires[i] for i in range(len(wires))})

diff --git a/poetry.lock b/poetry.lock
diff --git a/pyproject.toml b/pyproject.toml
@@ -42,7 +42,7 @@ pennylane = "0.38.0"
 pennylane-cirq = "0.36.0"
 pennylane-qiskit = "0.38.0"
 pennylane-qulacs = "0.36.0"
-pennylane-catalyst = "0.8.0"
+pennylane-catalyst = "0.8.1"
 ##########################################################