Draft: feature/vqls_classiq_implementation

quantum_linear_systems/implementations/vqls_classiq_implementation.py

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+"""VQLS implementation using Classiq"""
+import numpy as np
+from classiq import Model
+from classiq.applications.chemistry import HVAParameters
+from classiq.applications.combinatorial_optimization import (
+    # QAOAConfig,
+    OptimizerConfig,
+)
+from classiq.execution import OptimizerType
+
+# from classiq import construct_combinatorial_optimization_model
+
+matrix_a = np.random.rand(2, 2)
+
+
+# hamiltonian variational ansatz
+ansatz = HVAParameters(reps=3)
+# state preparation of b
+# matrix decomposition of A into linear combination of unitaries (vqls-prototype defaults to symmetric)
+
+# optimizer to minimize cost function
+optimizer_config = OptimizerConfig(opt_type=OptimizerType.COBYLA, max_iteration=250)
+# Note : maybe see https://pennylane.ai/qml/demos/tutorial_vqls
+hadamard_test_paulistring = 1
+Model.vqe(
+    hamiltonian=hadamard_test_paulistring,
+    optimizer=optimizer_config,
+    max_iteration=250,
+    maximize=False,
+    initial_point=ansatz,
+)
+
+# cost function
+# global_cost_function
+# local_cost_function
+#
+# # todo: fix this rest
+#
+# # Quantum circuit parameters
+# num_qubits = A.shape[0]
+# depth = 2
+# num_iterations = 100
+#
+# # Prepare initial state
+# initial_state = np.ones(num_qubits) / np.sqrt(num_qubits)
+#
+# # Create a quantum circuit (Classiq syntax might vary)
+# qc = QuantumCircuit(num_qubits)
+# qc.initialize(initial_state, range(num_qubits))
+#
+# # Variational form construction (Classiq syntax might vary)
+# for d in range(depth):
+#     for i in range(num_qubits):
+#         qc.rx(qc.circuit_parameters[f'rx_{d}_{i}'], i)
+#
+# # Define an optimizer (Classiq syntax might vary)
+# optimizer = Optimizer(max_iterations=num_iterations)
+#
+# # Define the Hamiltonian minimization problem (Classiq syntax might vary)
+# hamiltonian_problem = HamiltonianMinimizationProblem(
+#     ansatz=qc,  # Use your constructed quantum circuit
+#     hamiltonian=A,  # Use your matrix A
+#     target_vector=b,  # Use your vector b
+#     optimizer=optimizer
+# )
+#
+# # Execute the Hamiltonian minimization problem and obtain results (Classiq syntax might vary)
+# result = hamiltonian_problem.execute()
+#
+# # Extract the solution vector
+# solution_vector = result.optimal_parameters
+# solution = np.dot(A, solution_vector)
+#
+# print("Solution vector:", solution_vector)
+# print("Solution:", solution)