Create quantum_computer.py

quantum_computer/quantum_computer.py

@@ -0,0 +1,63 @@
+import numpy as np
+from qiskit import QuantumCircuit, execute
+
+class QuantumComputer:
+    def __init__(self, num_qubits):
+        self.num_qubits = num_qubits
+
+    def create_quantum_circuit(self):
+        """
+        Create a quantum circuit using Qiskit.
+
+        Returns:
+            QuantumCircuit: The created quantum circuit.
+        """
+        circuit = QuantumCircuit(self.num_qubits)
+        return circuit
+
+    def add_quantum_gates(self, circuit, gates):
+        """
+        Add quantum gates to a quantum circuit.
+
+        Args:
+            circuit (QuantumCircuit): The quantum circuit to add gates to.
+            gates (list): The quantum gates to add.
+        """
+        for gate in gates:
+            circuit.append(gate, [i for i in range(self.num_qubits)])
+
+    def execute_quantum_circuit(self, circuit):
+        """
+        Execute a quantum circuit using Qiskit.
+
+        Args:
+            circuit (QuantumCircuit): The quantum circuit to execute.
+
+        Returns:
+            numpy array: The result of the quantum circuit execution.
+        """
+        job = execute(circuit, backend="qasm_simulator")
+        result = job.result()
+        return result.get_statevector()
+
+    def optimize_quantum_circuit(self, circuit, objective_function):
+        """
+        Optimize a quantum circuit using a genetic algorithm.
+
+        Args:
+            circuit (QuantumCircuit): The quantum circuit to optimize.
+            objective_function (function): The objective function to optimize.
+
+        Returns:
+            QuantumCircuit: The optimized quantum circuit.
+        """
+        from scipy.optimize import differential_evolution
+        def objective_function_wrapper(params):
+            circuit.set_parameters(params)
+            result = execute(circuit, backend="qasm_simulator")
+            return objective_function(result.get_statevector())
+        bounds = [(0, 2 * np.pi) for _ in range(circuit.num_parameters)]
+        result = differential_evolution(objective_function_wrapper, bounds)
+        optimized_parameters = result.x
+        circuit.set_parameters(optimized_parameters)
+        return circuit