rabbitmq tests

2026-03-16 22:13:26 +03:00
parent 1d0ee54b1f
commit 0855a064c9
13 changed files with 437 additions and 489 deletions
--- a/source/vqe.py
+++ b/source/vqe.py
@@ -1,45 +1,63 @@
-from pennylane import numpy as np
-import pennylane as qml
-import os
-from multiprocessing import Queue
-
-os.environ["OMP_NUM_THREADS"] = '16'
-
-def get_sctructure_from_xyz_path(path: str):
-    return qml.qchem.read_structure("methane.xyz")
-
-def run_vqe(q: Queue, symbols, coordinates, active_electrons, active_orbitals, max_iterations, conv_tol, step_size):
-    molecule = qml.qchem.Molecule(symbols, coordinates, load_data=True)
-    H, qubits = qml.qchem.molecular_hamiltonian(molecule, active_electrons=active_electrons,
-        active_orbitals=active_orbitals)
-    dev = qml.device("lightning.qubit", wires=qubits)
-
-
-
-    singles, doubles = qml.qchem.excitations(active_electrons, qubits)
-    params =  np.array(np.zeros(len(singles) + len(doubles)), requires_grad=True)
-    @qml.qnode(dev)
-    def circuit(param, wires):
-            # Map excitations to the wires the UCCSD circuit will act on
-        s_wires, d_wires = qml.qchem.excitations_to_wires(singles, doubles)
-        qml.UCCSD(param, wires, s_wires=s_wires, d_wires=d_wires, init_state=qml.qchem.hf_state(active_electrons, qubits))
-        return qml.expval(H)
-
-    def cost_fn(param):
-        return circuit(param, wires=range(qubits))
-    
-    opt = qml.GradientDescentOptimizer(stepsize=step_size)
-    
-    for n in range(max_iterations):
-        # Take step
-        params, prev_energy = opt.step_and_cost(cost_fn, params)
-
-        energy = cost_fn(params)
-
-        # Calculate difference between new and old energies
-        conv = np.abs(energy - prev_energy)
-
-        q.put([n, energy,params])
-
-        if conv <= conv_tol:
-            break
+import os
+from multiprocessing import Queue
+
+import pennylane as qml
+from pennylane import numpy as np
+
+os.environ["OMP_NUM_THREADS"] = "16"
+
+
+def get_sctructure_from_xyz_path(path: str):
+    return qml.qchem.read_structure("methane.xyz")
+
+
+def run_vqe(
+    queue_callback: Queue,
+    symbols,
+    coordinates,
+    active_electrons,
+    active_orbitals,
+    max_iterations,
+    conv_tol,
+    step_size,
+):
+    molecule = qml.qchem.Molecule(symbols, coordinates, load_data=True)
+    H, qubits = qml.qchem.molecular_hamiltonian(
+        molecule, active_electrons=active_electrons, active_orbitals=active_orbitals
+    )
+    dev = qml.device("lightning.qubit", wires=qubits)
+
+    singles, doubles = qml.qchem.excitations(active_electrons, qubits)
+    params = np.array(np.zeros(len(singles) + len(doubles)), requires_grad=True)
+
+    @qml.qnode(dev)
+    def circuit(param, wires):
+        # Map excitations to the wires the UCCSD circuit will act on
+        s_wires, d_wires = qml.qchem.excitations_to_wires(singles, doubles)
+        qml.UCCSD(
+            param,
+            wires,
+            s_wires=s_wires,
+            d_wires=d_wires,
+            init_state=qml.qchem.hf_state(active_electrons, qubits),
+        )
+        return qml.expval(H)
+
+    def cost_fn(param):
+        return circuit(param, wires=range(qubits))
+
+    opt = qml.GradientDescentOptimizer(stepsize=step_size)
+
+    for n in range(max_iterations):
+        # Take step
+        params, prev_energy = opt.step_and_cost(cost_fn, params)
+
+        energy = cost_fn(params)
+
+        # Calculate difference between new and old energies
+        conv = np.abs(energy - prev_energy)
+
+        queue_callback.put([n, energy, params])
+
+        if conv <= conv_tol:
+            break