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}")