# qullm/qnn.py

import numpy as np

class QuantumNeuralNetwork:
    def __init__(self, num_qubits, num_layers):
        """
        Initialize the quantum neural network
        :param num_qubits: Number of qubits
        :param num_layers: Number of layers
        """
        self.num_qubits = num_qubits
        self.num_layers = num_layers
        self.weights = np.random.rand(num_layers, num_qubits, 3)  # Randomly initialize weights

    def simulate(self, input_data):
        """
        Simulate the computation of the quantum neural network
        :param input_data: Input data
        :return: Simulated output
        """
        if isinstance(input_data, list):
            input_data = np.array(input_data)
        
        if input_data.shape[0] != self.num_qubits:
            raise ValueError(f"Input data must have {self.num_qubits} elements.")
        
        # Initialize quantum state
        quantum_state = self.initialize_quantum_state(input_data)
        
        # Propagate quantum state through the network layers
        for layer in range(self.num_layers):
            quantum_state = self.apply_layer(quantum_state, self.weights[layer])
        
        # Measure quantum state
        output_data = self.measure_quantum_state(quantum_state)
        
        return output_data

    def initialize_quantum_state(self, input_data):
        """
        Initialize quantum state
        :param input_data: Input data
        :return: Initial quantum state
        """
        quantum_state = np.zeros((2**self.num_qubits,), dtype=complex)
        index = 0
        for i, val in enumerate(input_data):
            index += val * (2**i)
        quantum_state[index] = 1.0
        return quantum_state

    def apply_layer(self, quantum_state, weights):
        """
        Apply a layer of quantum operations
        :param quantum_state: Current quantum state
        :param weights: Weights of the current layer
        :return: Updated quantum state
        """
        # Here we use a simple example of applying rotation gates
        for qubit in range(self.num_qubits):
            theta = weights[qubit]
            quantum_state = self.apply_rotation(quantum_state, qubit, theta)
        return quantum_state

    def apply_rotation(self, quantum_state, qubit, theta):
        """
        Apply rotation gate
        :param quantum_state: Current quantum state
        :param qubit: Qubit index
        :param theta: Rotation angle
        :return: Updated quantum state
        """
        rotation_matrix = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]])
        identity = np.eye(2**qubit)
        full_rotation = np.kron(np.kron(identity, rotation_matrix), np.eye(2**(self.num_qubits-qubit-1)))
        return np.dot(full_rotation, quantum_state)

    def measure_quantum_state(self, quantum_state):
        """
        Measure quantum state
        :param quantum_state: Current quantum state
        :return: Measurement result
        """
        probabilities = np.abs(quantum_state)**2
        return np.argmax(probabilities)


# Example usage
if __name__ == '__main__':
    qnn = QuantumNeuralNetwork(num_qubits=3, num_layers=2)
    input_data = [1, 0, 1]
    output = qnn.simulate(input_data)
    print(f"Simulated output: {output}")