| import numpy as np | |
| from scipy.integrate import solve_ivp | |
| from qiskit import Aer, QuantumCircuit, execute | |
| from qiskit.algorithms import Grover, VQE | |
| from qiskit.circuit.library import EfficientSU2 | |
| from qiskit.utils import QuantumInstance | |
| from qiskit.algorithms.optimizers import COBYLA | |
| from matplotlib import pyplot as plt | |
| import pennylane as qml | |
| class UnifiedQuantumAgent: | |
| def __init__(self): | |
| # Initialize Quantum Backend | |
| self.backend = Aer.get_backend("qasm_simulator") | |
| self.quantum_instance = QuantumInstance(self.backend) | |
| # Quantum Optimization (Grover's Search) | |
| def grover_search(self, oracle_bits): | |
| n_qubits = len(oracle_bits) | |
| circuit = QuantumCircuit(n_qubits) | |
| circuit.z([i for i, bit in enumerate(oracle_bits) if bit == "1"]) | |
| circuit = Grover(circuit).construct_circuit(None) | |
| result = execute(circuit, self.backend, shots=1024).result() | |
| counts = result.get_counts() | |
| return counts | |
| # Variational Quantum Eigensolver (VQE) | |
| def vqe_solver(self, hamiltonian): | |
| ansatz = EfficientSU2(num_qubits=2, entanglement="linear") | |
| optimizer = COBYLA(maxiter=200) | |
| vqe = VQE(ansatz, optimizer, quantum_instance=self.quantum_instance) | |
| result = vqe.compute_minimum_eigenvalue(operator=hamiltonian) | |
| return result | |
| # Riemann Zeta Function Oscillation Simulation | |
| def riemann_oscillation(self, t_span, zeta_func): | |
| def oscillatory_rhs(t, y): | |
| return [np.sin(y[0]) - zeta_func(t)] | |
| solution = solve_ivp(oscillatory_rhs, t_span, [0], t_eval=np.linspace(t_span[0], t_span[1], 500)) | |
| return solution.t, solution.y[0] | |
| # PennyLane Quantum Circuit Simulation | |
| def pennylane_simulation(self, params): | |
| dev = qml.device("default.qubit", wires=2) | |
| @qml.qnode(dev) | |
| def circuit(params): | |
| qml.RX(params[0], wires=0) | |
| qml.RY(params[1], wires=1) | |
| qml.CNOT(wires=[0, 1]) | |
| return qml.expval(qml.PauliZ(0)) | |
| return circuit(params) | |
| # Visualization Utility | |
| def plot_results(self, x, y, title, xlabel, ylabel): | |
| plt.figure(figsize=(8, 6)) | |
| plt.plot(x, y, label="Simulation Data") | |
| plt.title(title) | |
| plt.xlabel(xlabel) | |
| plt.ylabel(ylabel) | |
| plt.legend() | |
| plt.grid(True) | |
| plt.show() | |
| # Unified Agent in Action | |
| if __name__ == "__main__": | |
| agent = UnifiedQuantumAgent() | |
| # Example 1: Grover's Search | |
| oracle = "1010" | |
| counts = agent.grover_search(oracle) | |
| print(f"Grover's Search Results: {counts}") | |
| # Example 2: VQE Solver | |
| from qiskit.opflow import I, Z | |
| hamiltonian = (Z ^ I) + (I ^ Z) | |
| vqe_result = agent.vqe_solver(hamiltonian) | |
| print(f"VQE Optimal Value: {vqe_result.eigenvalue.real}") | |
| # Example 3: Riemann Zeta Function Oscillation | |
| t, y = agent.riemann_oscillation((0, 10), lambda t: np.cos(t)) | |
| agent.plot_results(t, y, "Riemann Zeta Oscillation", "Time", "Amplitude") | |
| # Example 4: PennyLane Simulation | |
| params = [np.pi / 4, np.pi / 3] | |
| expectation = agent.pennylane_simulation(params) | |
| print(f"PennyLane Simulation Expectation Value: {expectation}") |