# ibm_integration.py

import jax
import jax.numpy as jnp
from jax import random
import qiskit
from qiskit import QuantumCircuit
from qiskit_aer import Aer
from qiskit_algorithms.minimum_eigensolvers import VQE, QAOA
from qiskit_algorithms.optimizers import COBYLA
from qiskit.circuit.library import TwoLocal
from qiskit.primitives import Sampler

class IBMIntegration:
    def __init__(self):
        pass

    def watson_nlp(self, text):
        # Placeholder for IBM Watson NLP functionality
        pass

    def quantum_circuit(self):
        # Placeholder for IBM Quantum Computing functionality
        pass

    def cloud_services(self):
        # Placeholder for IBM Cloud Services integration
        pass

def ibm_quantum_inspired_optimization(problem_matrix, num_qubits):
    """
    Perform quantum-inspired optimization using IBM's Qiskit library.

    Args:
    problem_matrix (jnp.array): The problem matrix to be optimized
    num_qubits (int): Number of qubits to use in the quantum circuit

    Returns:
    jnp.array: Optimized solution
    """
    # Convert JAX array to numpy for Qiskit compatibility
    problem_matrix_np = jnp.asarray(problem_matrix)

    # Set up the quantum sampler
    backend = Aer.get_backend('statevector_simulator')
    sampler = Sampler(backend)

    # Create a simple ansatz
    ansatz = TwoLocal(num_qubits, 'ry', 'cz', reps=3, entanglement='linear')

    # Set up the VQE algorithm
    optimizer = COBYLA(maxiter=100)
    vqe = VQE(ansatz, optimizer, sampler=sampler)

    # Run the VQE algorithm
    result = vqe.compute_minimum_eigenvalue(problem_matrix_np)

    # Convert the result back to a JAX array
    optimized_solution = jnp.array(result.optimal_point)

    return optimized_solution

def integrate_ibm_quantum(input_data):
    """
    Integrate IBM's quantum-inspired optimization into NeuroFlex.

    Args:
    input_data (jnp.array): Input data to be processed

    Returns:
    jnp.array: Processed data using IBM's quantum-inspired techniques
    """
    num_qubits = input_data.shape[-1]
    problem_matrix = jnp.dot(input_data.T, input_data)

    optimized_solution = ibm_quantum_inspired_optimization(problem_matrix, num_qubits)

    # Apply the optimized solution to the input data
    processed_data = jnp.dot(input_data, optimized_solution)

    return processed_data
