import numpy as np
from scipy.optimize import minimize
import multiprocessing as mp
from functools import partial
import itertools

class QuantumInspiredOptimizer:
    def __init__(self, problem_size, num_layers):
        self.problem_size = problem_size
        self.num_layers = num_layers
        
    def initialize_parameters(self):
        # Initialize gamma and beta parameters
        self.gamma = np.random.uniform(0, 2 * np.pi, self.num_layers)
        self.beta = np.random.uniform(0, np.pi, self.num_layers)
    
    def apply_mixing_hamiltonian(self, state):
        # Apply mixing Hamiltonian (X rotations)
        for i in range(self.problem_size):
            # Apply the mixing operation directly with NumPy handling the types
            cos_term = np.cos(self.beta[0])
            sin_term = np.sin(self.beta[0])
            state[i] = cos_term * state[i] + 1j * sin_term * (1 - state[i])
        return state
    
    def apply_problem_hamiltonian(self, state, cost_function):
        # Apply problem Hamiltonian (phase rotations based on cost function)
        for i in range(self.problem_size):
            # Create complex phase factor
            phase_factor = np.exp(1j * self.gamma[0] * cost_function(i))
            # Direct complex multiplication - NumPy handles the types
            state[i] = state[i] * phase_factor
        return state
    
    def optimize(self, cost_function, num_iterations):
        # Main optimization loop
        self.initialize_parameters()
        state = np.ones(self.problem_size) / np.sqrt(self.problem_size)
        
        for _ in range(num_iterations):
            for layer in range(self.num_layers):
                state = self.apply_problem_hamiltonian(state, cost_function)
                state = self.apply_mixing_hamiltonian(state)
            
            # Update parameters (simplified - you may want to use a more sophisticated update rule)
            self.gamma += np.random.normal(0, 0.1, self.num_layers)
            self.beta += np.random.normal(0, 0.1, self.num_layers)
        
        return np.argmax(np.abs(state)**2)  # Return the most probable solution

# Example usage
def cost_function(x):
    # Define your problem-specific cost function here
    return x**2

optimizer = QuantumInspiredOptimizer(problem_size=10, num_layers=2)
solution = optimizer.optimize(cost_function, num_iterations=100)
print(f"Optimized solution: {solution}")

def quantum_inspired_optimization(cost_function, n_bits, n_iterations):
    best_solution = None
    best_cost = float('inf')
    
    for _ in range(n_iterations):
        # Generate a random bitstring
        solution = np.random.randint(2, size=n_bits)
        
        # Evaluate the cost
        cost = cost_function(solution)
        
        # Update the best solution if necessary
        if cost < best_cost:
            best_solution = solution
            best_cost = cost
        
        # Apply quantum-inspired operations (e.g., bit-flip mutation)
        mutation_prob = 0.1
        solution = np.where(np.random.random(n_bits) < mutation_prob, 1 - solution, solution)
    
    return best_solution, best_cost

# Example usage
def example_cost_function(bitstring):
    # Example: minimize the number of 1s in the bitstring
    return np.sum(bitstring)

# Run the optimization
solution, cost = quantum_inspired_optimization(example_cost_function, n_bits=10, n_iterations=1000)
print(f"Best solution: {solution}")
print(f"Best cost: {cost}")

class ProblemType:
    MAXCUT = "maxcut"
    TSP = "tsp"
    QUBO = "qubo"

def qaoa_optimized(problem_type, problem_data, p, gamma, beta):
    """
    Generalized QAOA for different problem types
    
    :param problem_type: Type of the problem (ProblemType enum)
    :param problem_data: Data specific to the problem (e.g., graph for MaxCut, distance matrix for TSP)
    :param p: Number of QAOA steps
    :param gamma: List of gamma angles
    :param beta: List of beta angles
    :return: Final state and expectation value of the cost function
    """
    if problem_type == ProblemType.MAXCUT:
        return qaoa_maxcut_optimized(problem_data, p, gamma, beta)
    elif problem_type == ProblemType.TSP:
        return qaoa_tsp_optimized(problem_data, p, gamma, beta)
    elif problem_type == ProblemType.QUBO:
        return qaoa_qubo_optimized(problem_data, p, gamma, beta)
    else:
        raise ValueError("Unsupported problem type")

def qaoa_maxcut_optimized(graph, p, gamma, beta):
    """
    Optimized QAOA for MaxCut problem
    
    :param graph: Adjacency matrix of the graph
    :param p: Number of QAOA steps
    :param gamma: List of gamma angles
    :param beta: List of beta angles
    :return: Final state and expectation value of the cost function
    """
    n = len(graph)
    N = 2**n
    
    # Initialize state in superposition
    state = np.ones(N) / np.sqrt(N)
    
    # Precompute bit representations
    bit_representations = np.arange(N)[:, np.newaxis] & (1 << np.arange(n))
    bit_representations = (bit_representations > 0).astype(int)
    
    for step in range(p):
        # Problem unitary
        phase = np.zeros(N)
        for i in range(n):
            for j in range(i+1, n):
                if graph[i][j] == 1:
                    phase += (bit_representations[:, i] != bit_representations[:, j]) * gamma[step]
        # Use explicit complex number handling for phase application
        complex_phase = np.exp(-1j * phase)
        # Apply phase while preserving complex values
        state = state * complex_phase  # Use assignment instead of in-place multiplication
        
        # Mixer unitary
        for i in range(n):
            cos_beta = np.cos(beta[step])
            sin_beta = np.sin(beta[step])
            flip_mask = 1 << i
            state_flipped = state.reshape(-1)[(np.arange(N) ^ flip_mask)]
            state = cos_beta * state - 1j * sin_beta * state_flipped

    # Compute expectation value
    expectation = 0
    for i in range(n):
        for j in range(i+1, n):
            if graph[i][j] == 1:
                cut_mask = bit_representations[:, i] != bit_representations[:, j]
                expectation += np.sum(np.abs(state[cut_mask])**2)

    return state, expectation

def qaoa_tsp_optimized(distance_matrix, p, gamma, beta):
    """
    Optimized QAOA for Traveling Salesman Problem
    
    :param distance_matrix: Matrix of distances between cities
    :param p: Number of QAOA steps
    :param gamma: List of gamma angles
    :param beta: List of beta angles
    :return: Final state and expectation value of the cost function
    """
    n = len(distance_matrix)
    N = n**n  # Total number of possible routes
    
    # Initialize state in superposition
    state = np.ones(N) / np.sqrt(N)
    
    # Precompute route representations
    routes = np.array(list(itertools.permutations(range(n))))
    
    for step in range(p):
        # Problem unitary
        phase = np.zeros(N)
        for i in range(N):
            route = routes[i]
            cost = sum(distance_matrix[route[j]][route[(j+1)%n]] for j in range(n))
            phase[i] = cost * gamma[step]
        # Use explicit complex number handling for phase application
        complex_phase = np.exp(-1j * phase)
        # Apply phase while preserving complex values
        state = state * complex_phase  # Use assignment instead of in-place multiplication
        
        # Mixer unitary
        for i in range(n):
            for j in range(i+1, n):
                # Swap cities i and j in all routes
                swap_mask = routes[:, i] != routes[:, j]
                routes[swap_mask, i], routes[swap_mask, j] = routes[swap_mask, j], routes[swap_mask, i]
                
                cos_beta = np.cos(beta[step])
                sin_beta = np.sin(beta[step])
                state_swapped = state[swap_mask]
                state[swap_mask] = cos_beta * state[swap_mask] - 1j * sin_beta * state_swapped
                state[~swap_mask] = cos_beta * state[~swap_mask] - 1j * sin_beta * state[~swap_mask]

    # Compute expectation value
    expectation = 0
    for i in range(N):
        route = routes[i]
        cost = sum(distance_matrix[route[j]][route[(j+1)%n]] for j in range(n))
        expectation += cost * np.abs(state[i])**2

    return state, -expectation  # Return positive expectation here

def qaoa_qubo_optimized(qubo_matrix, p, gamma, beta):
    """
    Optimized QAOA for Quadratic Unconstrained Binary Optimization
    
    :param qubo_matrix: QUBO matrix
    :param p: Number of QAOA steps
    :param gamma: List of gamma angles
    :param beta: List of beta angles
    :return: Final state and expectation value of the cost function
    """
    n = len(qubo_matrix)
    N = 2**n
    
    # Initialize state in superposition
    state = np.ones(N) / np.sqrt(N)
    
    # Precompute bit representations
    bit_representations = np.arange(N)[:, np.newaxis] & (1 << np.arange(n))
    bit_representations = (bit_representations > 0).astype(int)
    
    for step in range(p):
        # Problem unitary
        phase = np.zeros(N)
        for i in range(n):
            for j in range(n):
                phase += qubo_matrix[i][j] * bit_representations[:, i] * bit_representations[:, j] * gamma[step]
        # Use explicit complex number handling for phase application
        complex_phase = np.exp(-1j * phase)
        # Apply phase while preserving complex values
        state = state * complex_phase  # Use assignment instead of in-place multiplication
        
        # Mixer unitary
        for i in range(n):
            cos_beta = np.cos(beta[step])
            sin_beta = np.sin(beta[step])
            flip_mask = 1 << i
            state_flipped = state.reshape(-1)[(np.arange(N) ^ flip_mask)]
            state = cos_beta * state - 1j * sin_beta * state_flipped

    # Compute expectation value
    expectation = 0
    for i in range(N):
        cost = np.sum(qubo_matrix * np.outer(bit_representations[i], bit_representations[i]))
        expectation += cost * np.abs(state[i])**2

    return state, expectation

def objective_function(params, problem_type, problem_data, p):
    """
    Objective function for COBYLA optimizer.
    We want to maximize the expectation value, so we return its negative.
    """
    gamma = params[:p]
    beta = params[p:]
    return -qaoa_optimized(problem_type, problem_data, p, gamma, beta)[1]

def optimize_angles(problem_type, problem_data, p, initial_gamma=None, initial_beta=None, max_iterations=1000):
    """
    Use COBYLA to find optimal gamma and beta angles.
    
    :param problem_type: Type of the problem (ProblemType enum)
    :param problem_data: Data specific to the problem
    :param p: Number of QAOA steps
    :param initial_gamma: Initial gamma angles (optional)
    :param initial_beta: Initial beta angles (optional)
    :param max_iterations: Maximum number of iterations for optimization
    :return: Optimized gamma and beta angles, and the final expectation value
    """
    if problem_type == ProblemType.MAXCUT:
        n = len(problem_data)
    elif problem_type == ProblemType.TSP:
        n = len(problem_data)
    elif problem_type == ProblemType.QUBO:
        n = len(problem_data)
    else:
        raise ValueError("Unsupported problem type")
    
    if initial_gamma is None:
        initial_gamma = np.random.uniform(0, 2 * np.pi, p)
    if initial_beta is None:
        initial_beta = np.random.uniform(0, np.pi, p)
    
    initial_params = np.concatenate([initial_gamma, initial_beta])
    
    # Define bounds for gamma (0 to 2π) and beta (0 to π)
    bounds = [(0, 2*np.pi)] * p + [(0, np.pi)] * p
    
    result = minimize(
        objective_function,
        initial_params,
        args=(problem_type, problem_data, p),
        method='COBYLA',
        options={'maxiter': max_iterations},
        bounds=bounds
    )
    
    optimized_gamma = result.x[:p]
    optimized_beta = result.x[p:]
    final_expectation = -result.fun  # Remember, we minimized the negative expectation
    
    return optimized_gamma, optimized_beta, final_expectation

def parallel_optimize(problem_type, problem_data, p, num_trials):
    """
    Perform parallel optimization trials
    """
    with mp.Pool() as pool:
        results = pool.map(partial(optimize_angles, problem_type, problem_data, p), range(num_trials))
    return max(results, key=lambda x: x[2])

def adaptive_qaoa_optimize(problem_type, problem_data, initial_p, max_p, improvement_threshold, max_iterations):
    """
    Adaptive QAOA optimization that adjusts circuit depth (p) during optimization
    
    :param problem_type: Type of the problem (ProblemType enum)
    :param problem_data: Data specific to the problem
    :param initial_p: Initial number of QAOA steps
    :param max_p: Maximum allowed number of QAOA steps
    :param improvement_threshold: Minimum improvement required to increase p
    :param max_iterations: Maximum number of iterations for each p
    :return: Optimized gamma and beta angles, final expectation value, and final p
    """
    p = initial_p
    best_expectation = float('-inf')
    best_gamma = None
    best_beta = None
    
    while p <= max_p:
        gamma, beta, expectation = optimize_angles(problem_type, problem_data, p, max_iterations=max_iterations)
        
        relative_improvement = (expectation - best_expectation) / abs(best_expectation) if best_expectation != 0 else float('inf')
        
        if relative_improvement > improvement_threshold:
            best_expectation = expectation
            best_gamma = gamma
            best_beta = beta
            p += 1
        else:
            break
    
    return best_gamma, best_beta, best_expectation, p - 1

def parallel_adaptive_optimize(problem_type, problem_data, initial_p, max_p, improvement_threshold, max_iterations, num_trials):
    """
    Perform parallel adaptive optimization trials
    """
    with mp.Pool() as pool:
        results = pool.map(
            partial(adaptive_qaoa_optimize, problem_type, problem_data, initial_p, max_p, improvement_threshold, max_iterations),
            range(num_trials)
        )
    return max(results, key=lambda x: x[2])

def extract_solution_maxcut(state, graph):
    """
    Extract the most probable MaxCut solution from the final state
    
    :param state: Final quantum state
    :param graph: Adjacency matrix of the graph
    :return: Most probable cut (binary string) and its probability
    """
    n = len(graph)
    N = 2**n
    
    probabilities = np.abs(state)**2
    most_probable_index = np.argmax(probabilities)
    
    # Convert the index to binary string
    cut = format(most_probable_index, f'0{n}b')
    probability = probabilities[most_probable_index]
    
    return cut, probability

def extract_solution_tsp(state, distance_matrix):
    """
    Extract the most probable TSP solution from the final state
    
    :param state: Final quantum state
    :param distance_matrix: Matrix of distances between cities
    :return: Most probable route (as a list of city indices) and its probability
    """
    n = len(distance_matrix)
    N = n**n
    
    probabilities = np.abs(state)**2
    most_probable_index = np.argmax(probabilities)
    
    # Convert the index to a permutation
    route = list(np.unravel_index(most_probable_index, (n,) * n))
    probability = probabilities[most_probable_index]
    
    return route, probability

def extract_solution_qubo(state, qubo_matrix):
    """
    Extract the most probable QUBO solution from the final state
    
    :param state: Final quantum state
    :param qubo_matrix: QUBO matrix
    :return: Most probable solution (binary string) and its probability
    """
    n = len(qubo_matrix)
    N = 2**n
    
    probabilities = np.abs(state)**2
    most_probable_index = np.argmax(probabilities)
    
    # Convert the index to binary string
    solution = format(most_probable_index, f'0{n}b')
    probability = probabilities[most_probable_index]
    
    return solution, probability

def main():
    # Define a larger graph for testing
    n = 20  # number of nodes
    graph = np.random.randint(0, 2, (n, n))
    graph = (graph + graph.T) // 2  # make it symmetric
    np.fill_diagonal(graph, 0)  # no self-loops

    initial_p = 1
    max_p = 5
    improvement_threshold = 0.01
    max_iterations = 1000
    num_trials = 10  # Number of parallel optimization trials

    # Run the parallel adaptive optimization
    optimized_gamma, optimized_beta, final_expectation, final_p = parallel_adaptive_optimize(
        ProblemType.MAXCUT, graph, initial_p, max_p, improvement_threshold, max_iterations, num_trials
    )

    print(f"Optimized gamma angles: {optimized_gamma}")
    print(f"Optimized beta angles: {optimized_beta}")
    print(f"Final MaxCut expectation value: {final_expectation}")
    print(f"Final p: {final_p}")

    # Compare with random angles at p=1
    random_gamma = np.random.uniform(0, 2 * np.pi, 1)
    random_beta = np.random.uniform(0, np.pi, 1)
    random_expectation = qaoa_optimized(ProblemType.MAXCUT, graph, 1, random_gamma, random_beta)[1]

    print(f"\nRandom angles expectation value (p=1): {random_expectation}")
    print(f"Improvement: {(final_expectation - random_expectation) / random_expectation * 100:.2f}%")

    # Add TSP example with adaptive QAOA
    print("\nTSP Example (Adaptive QAOA):")
    n_cities = 5
    distance_matrix = np.random.rand(n_cities, n_cities)
    np.fill_diagonal(distance_matrix, 0)
    distance_matrix = (distance_matrix + distance_matrix.T) / 2  # Make symmetric

    tsp_optimized_gamma, tsp_optimized_beta, tsp_final_expectation, tsp_final_p = parallel_adaptive_optimize(
        ProblemType.TSP, distance_matrix, initial_p, max_p, improvement_threshold, max_iterations, num_trials
    )

    print(f"TSP Optimized gamma angles: {tsp_optimized_gamma}")
    print(f"TSP Optimized beta angles: {tsp_optimized_beta}")
    print(f"TSP Final expectation value: {tsp_final_expectation}")
    print(f"TSP Final p: {tsp_final_p}")

    # Add QUBO example with adaptive QAOA
    print("\nQUBO Example (Adaptive QAOA):")
    n_variables = 5
    qubo_matrix = np.random.rand(n_variables, n_variables)
    qubo_matrix = (qubo_matrix + qubo_matrix.T) / 2  # Make symmetric

    qubo_optimized_gamma, qubo_optimized_beta, qubo_final_expectation, qubo_final_p = parallel_adaptive_optimize(
        ProblemType.QUBO, qubo_matrix, initial_p, max_p, improvement_threshold, max_iterations, num_trials
    )

    print(f"QUBO Optimized gamma angles: {qubo_optimized_gamma}")
    print(f"QUBO Optimized beta angles: {qubo_optimized_beta}")
    print(f"QUBO Final expectation value: {qubo_final_expectation}")
    print(f"QUBO Final p: {qubo_final_p}")

    # MaxCut example
    print("\nMaxCut Example:")
    maxcut_state, maxcut_expectation = qaoa_optimized(ProblemType.MAXCUT, graph, final_p, optimized_gamma, optimized_beta)
    maxcut_solution, maxcut_probability = extract_solution_maxcut(maxcut_state, graph)
    print(f"MaxCut Solution: {maxcut_solution}")
    print(f"MaxCut Solution Probability: {maxcut_probability}")
    print(f"MaxCut Expectation Value: {maxcut_expectation}")

    # TSP example
    print("\nTSP Example:")
    tsp_state, tsp_expectation = qaoa_optimized(ProblemType.TSP, distance_matrix, tsp_final_p, tsp_optimized_gamma, tsp_optimized_beta)
    tsp_solution, tsp_probability = extract_solution_tsp(tsp_state, distance_matrix)
    print(f"TSP Solution: {tsp_solution}")
    print(f"TSP Solution Probability: {tsp_probability}")
    print(f"TSP Expectation Value: {tsp_expectation}")

    # QUBO example
    print("\nQUBO Example:")
    qubo_state, qubo_expectation = qaoa_optimized(ProblemType.QUBO, qubo_matrix, qubo_final_p, qubo_optimized_gamma, qubo_optimized_beta)
    qubo_solution, qubo_probability = extract_solution_qubo(qubo_state, qubo_matrix)
    print(f"QUBO Solution: {qubo_solution}")
    print(f"QUBO Solution Probability: {qubo_probability}")
    print(f"QUBO Expectation Value: {qubo_expectation}")

if __name__ == "__main__":
    main()