Create quantum_solver.py

Author: KOSASIH

quantum-ai-arbitration/src/main/quantum/quantum_solver.py

@@ -0,0 +1,84 @@
+# src/main/quantum/quantum_solver.py
+
+from qiskit import QuantumCircuit, Aer, execute
+from qiskit.visualization import plot_histogram
+import numpy as np
+import logging
+
+class QuantumSolver:
+    def __init__(self, backend='qasm_simulator', shots=1024):
+        self.backend = backend
+        self.shots = shots
+        self.aer_simulator = Aer.get_backend(self.backend)
+        logging.info("QuantumSolver initialized with backend: %s and shots: %d", self.backend, self.shots)
+
+    def solve_optimization(self, problem):
+        """
+        Solve an optimization problem using a quantum algorithm.
+        
+        Args:
+            problem (list): A list of values representing the optimization problem.
+
+        Returns:
+            dict: A dictionary containing the optimal solution and its value.
+        """
+        logging.info("Starting optimization with problem: %s", problem)
+
+        # Create a quantum circuit
+        num_qubits = len(problem)
+        qc = QuantumCircuit(num_qubits)
+
+        # Initialize the quantum circuit (example: prepare a superposition)
+        qc.h(range(num_qubits))
+
+        # Apply a mock optimization algorithm (e.g., Grover's algorithm)
+        # This is a placeholder for a real quantum optimization algorithm
+        for i in range(num_qubits):
+            qc.x(i)  # Example operation: flip the qubit
+
+        # Measure the qubits
+        qc.measure_all()
+
+        # Execute the quantum circuit
+        logging.info("Executing quantum circuit...")
+        job = execute(qc, backend=self.aer_simulator, shots=self.shots)
+        result = job.result()
+        counts = result.get_counts(qc)
+
+        # Analyze results to find the optimal solution
+        optimal_solution = max(counts, key=counts.get)
+        optimal_value = self.evaluate_solution(optimal_solution, problem)
+
+        logging.info("Optimization complete. Optimal solution: %s, Value: %d", optimal_solution, optimal_value)
+
+        return {
+            "solution": optimal_solution,
+            "value": optimal_value
+        }
+
+    def evaluate_solution(self, solution, problem):
+        """
+        Evaluate the solution to determine its value.
+        
+        Args:
+            solution (str): The binary string representing the solution.
+            problem (list): The original problem data.
+
+        Returns:
+            int: The evaluated value of the solution.
+        """
+        # Convert binary solution to integer
+        index = int(solution, 2)
+        if index < len(problem):
+            return problem[index]
+        else:
+            return 0  # Default value if index is out of bounds
+
+    def visualize_results(self, counts):
+        """
+        Visualize the results of the quantum circuit execution.
+        
+        Args:
+            counts (dict): The counts of measurement results.
+        """
+        plot_histogram(counts).show()