Create quantum_annealing_autonomous_systems.py

evolution/machine_learning/quantum_annealing_autonomous_systems.py

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+# quantum_annealing_autonomous_systems.py
+import numpy as np
+from qiskit import QuantumCircuit, execute
+from qiskit.providers.aer import AerSimulator
+
+class QuantumAnnealingAutonomousSystems:
+    def __init__(self, num_qubits, num_iterations):
+        self.num_qubits = num_qubits
+        self.num_iterations = num_iterations
+        self.quantum_backend = AerSimulator()
+        self.quantum_circuit = QuantumCircuit(num_qubits)
+
+    def _generate_quantum_circuit(self):
+        for i in range(self.num_qubits):
+            self.quantum_circuit.ry(np.pi / 2, i)
+            self.quantum_circuit.rz(np.pi / 2, i)
+        return self.quantum_circuit
+
+    def _evaluate_energy(self, X):
+        # Implement energy function
+        pass
+
+    def _anneal(self, X):
+        quantum_circuit = self._generate_quantum_circuit()
+        for i in range(self.num_iterations):
+            beta = i / self.num_iterations
+            quantum_circuit.ry(beta * np.pi / 2, 0)
+            quantum_circuit.rz(beta * np.pi / 2,  0)
+        job = execute(quantum_circuit, self.quantum_backend, shots=1024)
+        result = job.result()
+        counts = result.get_counts(quantum_circuit)
+        return counts
+
+    def train(self, X):
+        counts = self._anneal(X)
+        best_solution = max(counts, key=lambda x: self._evaluate_energy(x))
+        return best_solution
+
+# Example usage:
+X = np.random.rand(100, 10)
+qaas = QuantumAnnealingAutonomousSystems(num_qubits=10, num_iterations=100)
+best_solution = qaas.train(X)