quantum_agent.py

import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from typing import Dict, List, Tuple, Any, Optional
import logging
from dataclasses import dataclass
from scipy.optimize import minimize
import json

logger = logging.getLogger(__name__)

@dataclass
class QuantumState:
    """Represents a quantum state."""
    amplitudes: np.ndarray
    num_qubits: int
    fidelity: float = 1.0
    
    def __post_init__(self):
        """Normalize amplitudes after initialization."""
        self.amplitudes = self.amplitudes / np.linalg.norm(self.amplitudes)

@dataclass
class QuantumCircuit:
    """Represents a quantum circuit."""
    gates: List[str]
    parameters: np.ndarray
    num_qubits: int
    depth: int
    
    def __post_init__(self):
        """Initialize circuit properties."""
        if len(self.gates) == 0:
            # Generate some default gates for demonstration
            gate_types = ['RX', 'RY', 'RZ', 'CNOT', 'H']
            self.gates = [np.random.choice(gate_types) for _ in range(self.depth)]

class QuantumNeuralNetwork(nn.Module):
    """Neural network for quantum parameter optimization."""
    
    def __init__(self, input_dim: int, hidden_dim: int = 64, output_dim: int = 1):
        super().__init__()
        self.network = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, output_dim)
        )
    
    def forward(self, x):
        return self.network(x)

class ErrorMitigationNetwork(nn.Module):
    """Neural network for quantum error mitigation."""
    
    def __init__(self, state_dim: int, hidden_dim: int = 128):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Linear(state_dim * 2, hidden_dim),  # *2 for real and imaginary parts
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU()
        )
        
        self.decoder = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, state_dim * 2),
            nn.Tanh()
        )
    
    def forward(self, x):
        encoded = self.encoder(x)
        decoded = self.decoder(encoded)
        return decoded

class QuantumAIAgent:
    """AI agent for quantum computing optimization."""
    
    def __init__(self):
        """Initialize the quantum AI agent."""
        self.optimization_history = []
        self.error_mitigation_net = None
        self.parameter_optimizer = None
        logger.info("QuantumAIAgent initialized")
    
    def optimize_quantum_algorithm(self, algorithm: str, hamiltonian: np.ndarray, 
                                 initial_params: np.ndarray) -> Dict[str, Any]:
        """Optimize quantum algorithm parameters."""
        logger.info(f"Optimizing {algorithm} algorithm")
        
        if algorithm == "VQE":
            return self._optimize_vqe(hamiltonian, initial_params)
        elif algorithm == "QAOA":
            return self._optimize_qaoa(hamiltonian, initial_params)
        else:
            raise ValueError(f"Unknown algorithm: {algorithm}")
    
    def _optimize_vqe(self, hamiltonian: np.ndarray, initial_params: np.ndarray) -> Dict[str, Any]:
        """Optimize VQE parameters."""
        def objective(params):
            # Simulate VQE energy calculation
            # In practice, this would involve quantum circuit simulation
            circuit_result = self._simulate_vqe_circuit(params, hamiltonian)
            return circuit_result
        
        # Use classical optimization
        result = minimize(objective, initial_params, method='BFGS')
        
        # Create optimal circuit
        optimal_circuit = QuantumCircuit(
            gates=[],
            parameters=result.x,
            num_qubits=int(np.log2(hamiltonian.shape[0])),
            depth=len(result.x) // 2
        )
        
        return {
            'ground_state_energy': result.fun,
            'optimization_success': result.success,
            'iterations': result.nit,
            'optimal_parameters': result.x,
            'optimal_circuit': optimal_circuit
        }
    
    def _optimize_qaoa(self, hamiltonian: np.ndarray, initial_params: np.ndarray) -> Dict[str, Any]:
        """Optimize QAOA parameters."""
        num_layers = len(initial_params) // 2
        
        def objective(params):
            beta = params[:num_layers]
            gamma = params[num_layers:]
            return self._simulate_qaoa_circuit(beta, gamma, hamiltonian)
        
        result = minimize(objective, initial_params, method='COBYLA')
        
        return {
            'optimal_value': -result.fun,  # Minimize negative for maximization
            'optimization_success': result.success,
            'iterations': result.nit,
            'optimal_beta': result.x[:num_layers],
            'optimal_gamma': result.x[num_layers:]
        }
    
    def _simulate_vqe_circuit(self, params: np.ndarray, hamiltonian: np.ndarray) -> float:
        """Simulate VQE circuit and return energy expectation."""
        # Simplified simulation - create parameterized state
        num_qubits = int(np.log2(hamiltonian.shape[0]))
        
        # Create a parameterized quantum state (simplified)
        angles = params[:num_qubits]
        state = np.zeros(2**num_qubits, dtype=complex)
        
        # Simple parameterization: each qubit gets a rotation
        for i in range(2**num_qubits):
            amplitude = 1.0
            for q in range(num_qubits):
                if (i >> q) & 1:
                    amplitude *= np.sin(angles[q % len(angles)])
                else:
                    amplitude *= np.cos(angles[q % len(angles)])
            state[i] = amplitude
        
        # Normalize
        state = state / np.linalg.norm(state)
        
        # Calculate expectation value
        energy = np.real(np.conj(state).T @ hamiltonian @ state)
        return energy
    
    def _simulate_qaoa_circuit(self, beta: np.ndarray, gamma: np.ndarray, hamiltonian: np.ndarray) -> float:
        """Simulate QAOA circuit and return objective value."""
        # Simplified QAOA simulation
        num_qubits = int(np.log2(hamiltonian.shape[0]))
        
        # Start with uniform superposition
        state = np.ones(2**num_qubits, dtype=complex) / np.sqrt(2**num_qubits)
        
        # Apply QAOA layers (simplified)
        for i in range(len(beta)):
            # Problem Hamiltonian evolution (simplified)
            phase_factors = np.exp(-1j * gamma[i] * np.diag(hamiltonian))
            state = phase_factors * state
            
            # Mixer Hamiltonian evolution (simplified X rotations)
            # This is a very simplified version
            for q in range(num_qubits):
                # Apply rotation (simplified)
                rotation_factor = np.cos(beta[i]) + 1j * np.sin(beta[i])
                state = state * rotation_factor
        
        # Normalize
        state = state / np.linalg.norm(state)
        
        # Calculate expectation value
        expectation = np.real(np.conj(state).T @ hamiltonian @ state)
        return -expectation  # Return negative for minimization
    
    def mitigate_errors(self, quantum_state: QuantumState, noise_model: Dict[str, Any]) -> QuantumState:
        """Apply AI-powered error mitigation."""
        logger.info("Applying error mitigation")
        
        # Initialize error mitigation network if not exists
        if self.error_mitigation_net is None:
            state_dim = len(quantum_state.amplitudes)
            self.error_mitigation_net = ErrorMitigationNetwork(state_dim)
        
        # Convert quantum state to real input (real and imaginary parts)
        state_real = np.real(quantum_state.amplitudes)
        state_imag = np.imag(quantum_state.amplitudes)
        input_data = np.concatenate([state_real, state_imag])
        
        # Apply noise simulation
        noise_factor = noise_model.get('noise_factor', 0.1)
        noisy_input = input_data + np.random.normal(0, noise_factor, input_data.shape)
        
        # Apply error mitigation (simplified - in practice would be trained)
        with torch.no_grad():
            input_tensor = torch.FloatTensor(noisy_input).unsqueeze(0)
            corrected_output = self.error_mitigation_net(input_tensor).squeeze(0).numpy()
        
        # Convert back to complex amplitudes
        mid_point = len(corrected_output) // 2
        corrected_real = corrected_output[:mid_point]
        corrected_imag = corrected_output[mid_point:]
        corrected_amplitudes = corrected_real + 1j * corrected_imag
        
        # Normalize
        corrected_amplitudes = corrected_amplitudes / np.linalg.norm(corrected_amplitudes)
        
        # Calculate improved fidelity
        original_fidelity = quantum_state.fidelity
        fidelity_improvement = min(0.1, noise_factor * 0.5)  # Simplified improvement
        new_fidelity = min(1.0, original_fidelity + fidelity_improvement)
        
        return QuantumState(
            amplitudes=corrected_amplitudes,
            num_qubits=quantum_state.num_qubits,
            fidelity=new_fidelity
        )
    
    def optimize_resources(self, circuits: List[QuantumCircuit], available_qubits: int) -> Dict[str, Any]:
        """Optimize quantum resource allocation."""
        logger.info(f"Optimizing resources for {len(circuits)} circuits with {available_qubits} qubits")
        
        # Simple scheduling algorithm
        schedule = []
        current_time = 0
        total_qubits_used = 0
        
        # Sort circuits by qubit requirement (First-Fit Decreasing)
        sorted_circuits = sorted(enumerate(circuits), key=lambda x: x[1].num_qubits, reverse=True)
        
        for circuit_id, circuit in sorted_circuits:
            if circuit.num_qubits <= available_qubits:
                # Estimate execution time based on circuit depth
                estimated_duration = circuit.depth * 0.1  # 0.1 time units per gate
                
                schedule.append({
                    'circuit_id': circuit_id,
                    'qubits_allocated': circuit.num_qubits,
                    'start_time': current_time,
                    'estimated_duration': estimated_duration
                })
                
                current_time += estimated_duration
                total_qubits_used += circuit.num_qubits
        
        # Calculate resource utilization
        max_possible_qubits = len(circuits) * available_qubits
        resource_utilization = total_qubits_used / max_possible_qubits if max_possible_qubits > 0 else 0
        
        return {
            'schedule': schedule,
            'resource_utilization': resource_utilization,
            'estimated_runtime': current_time,
            'circuits_scheduled': len(schedule)
        }
    
    def hybrid_processing(self, classical_data: np.ndarray, quantum_component: str) -> Dict[str, Any]:
        """Perform hybrid quantum-classical processing."""
        logger.info(f"Running hybrid processing with {quantum_component}")
        
        # Preprocess classical data
        preprocessed_data = self._preprocess_classical_data(classical_data)
        
        # Apply quantum component
        if quantum_component == "quantum_kernel":
            quantum_result = self._apply_quantum_kernel(preprocessed_data)
        elif quantum_component == "quantum_feature_map":
            quantum_result = self._apply_quantum_feature_map(preprocessed_data)
        elif quantum_component == "quantum_neural_layer":
            quantum_result = self._apply_quantum_neural_layer(preprocessed_data)
        else:
            raise ValueError(f"Unknown quantum component: {quantum_component}")
        
        # Post-process results
        final_result = self._postprocess_quantum_result(quantum_result)
        
        return {
            'preprocessed_data': preprocessed_data,
            'quantum_result': quantum_result,
            'final_result': final_result
        }
    
    def _preprocess_classical_data(self, data: np.ndarray) -> np.ndarray:
        """Preprocess classical data for quantum processing."""
        # Normalize data
        normalized_data = (data - np.mean(data)) / (np.std(data) + 1e-8)
        
        # Apply some classical preprocessing
        processed_data = np.tanh(normalized_data)  # Squash to [-1, 1]
        
        return processed_data
    
    def _apply_quantum_kernel(self, data: np.ndarray) -> np.ndarray:
        """Apply quantum kernel transformation."""
        # Simulate quantum kernel computation
        # In practice, this would involve quantum feature maps
        kernel_matrix = np.zeros((len(data), len(data)))
        
        for i in range(len(data)):
            for j in range(len(data)):
                # Simplified quantum kernel (RBF-like with quantum enhancement)
                diff = data[i] - data[j]
                quantum_enhancement = np.cos(np.pi * diff) * np.exp(-0.5 * diff**2)
                kernel_matrix[i, j] = quantum_enhancement
        
        return kernel_matrix
    
    def _apply_quantum_feature_map(self, data: np.ndarray) -> np.ndarray:
        """Apply quantum feature map."""
        # Simulate quantum feature mapping
        num_features = len(data)
        quantum_features = np.zeros(num_features * 2)  # Expand feature space
        
        for i, x in enumerate(data):
            # Simulate quantum feature encoding
            quantum_features[2*i] = np.cos(np.pi * x)
            quantum_features[2*i + 1] = np.sin(np.pi * x)
        
        return quantum_features
    
    def _apply_quantum_neural_layer(self, data: np.ndarray) -> np.ndarray:
        """Apply quantum neural network layer."""
        # Simulate quantum neural network layer
        output_size = len(data)
        quantum_output = np.zeros(output_size)
        
        # Simplified quantum neural transformation
        for i, x in enumerate(data):
            # Simulate parameterized quantum circuit
            theta = x * np.pi / 4  # Parameter encoding
            quantum_output[i] = np.cos(theta) * np.exp(-0.1 * x**2)
        
        return quantum_output
    
    def _postprocess_quantum_result(self, quantum_result: np.ndarray) -> Dict[str, Any]:
        """Post-process quantum results."""
        # Calculate statistics
        stats = {
            'mean': np.mean(quantum_result),
            'std': np.std(quantum_result),
            'min': np.min(quantum_result),
            'max': np.max(quantum_result)
        }
        
        # Calculate confidence (simplified)
        confidence = 1.0 - np.std(quantum_result) / (np.abs(np.mean(quantum_result)) + 1e-8)
        confidence = max(0, min(1, confidence))
        
        return {
            'statistics': stats,
            'confidence': confidence,
            'processed_data': quantum_result
        }