CCockrum/Quantum-Optimization-Agent
0
1import numpy as np2import torch3import torch.nn as nn4import torch.optim as optim5from typing import Dict, List, Tuple, Any, Optional6import logging7from dataclasses import dataclass8from scipy.optimize import minimize9import json10 11logger = logging.getLogger(__name__)12 13@dataclass14class QuantumState:15 """Represents a quantum state."""16 amplitudes: np.ndarray17 num_qubits: int18 fidelity: float = 1.019 20 def __post_init__(self):21 """Normalize amplitudes after initialization."""22 self.amplitudes = self.amplitudes / np.linalg.norm(self.amplitudes)23 24@dataclass25class QuantumCircuit:26 """Represents a quantum circuit."""27 gates: List[str]28 parameters: np.ndarray29 num_qubits: int30 depth: int31 32 def __post_init__(self):33 """Initialize circuit properties."""34 if len(self.gates) == 0:35 # Generate some default gates for demonstration36 gate_types = ['RX', 'RY', 'RZ', 'CNOT', 'H']37 self.gates = [np.random.choice(gate_types) for _ in range(self.depth)]38 39class QuantumNeuralNetwork(nn.Module):40 """Neural network for quantum parameter optimization."""41 42 def __init__(self, input_dim: int, hidden_dim: int = 64, output_dim: int = 1):43 super().__init__()44 self.network = nn.Sequential(45 nn.Linear(input_dim, hidden_dim),46 nn.ReLU(),47 nn.Linear(hidden_dim, hidden_dim),48 nn.ReLU(),49 nn.Linear(hidden_dim, output_dim)50 )51 52 def forward(self, x):53 return self.network(x)54 55class ErrorMitigationNetwork(nn.Module):56 """Neural network for quantum error mitigation."""57 58 def __init__(self, state_dim: int, hidden_dim: int = 128):59 super().__init__()60 self.encoder = nn.Sequential(61 nn.Linear(state_dim * 2, hidden_dim), # *2 for real and imaginary parts62 nn.ReLU(),63 nn.Linear(hidden_dim, hidden_dim),64 nn.ReLU()65 )66 67 self.decoder = nn.Sequential(68 nn.Linear(hidden_dim, hidden_dim),69 nn.ReLU(),70 nn.Linear(hidden_dim, state_dim * 2),71 nn.Tanh()72 )73 74 def forward(self, x):75 encoded = self.encoder(x)76 decoded = self.decoder(encoded)77 return decoded78 79class QuantumAIAgent:80 """AI agent for quantum computing optimization."""81 82 def __init__(self):83 """Initialize the quantum AI agent."""84 self.optimization_history = []85 self.error_mitigation_net = None86 self.parameter_optimizer = None87 logger.info("QuantumAIAgent initialized")88 89 def optimize_quantum_algorithm(self, algorithm: str, hamiltonian: np.ndarray, 90 initial_params: np.ndarray) -> Dict[str, Any]:91 """Optimize quantum algorithm parameters."""92 logger.info(f"Optimizing {algorithm} algorithm")93 94 if algorithm == "VQE":95 return self._optimize_vqe(hamiltonian, initial_params)96 elif algorithm == "QAOA":97 return self._optimize_qaoa(hamiltonian, initial_params)98 else:99 raise ValueError(f"Unknown algorithm: {algorithm}")100 101 def _optimize_vqe(self, hamiltonian: np.ndarray, initial_params: np.ndarray) -> Dict[str, Any]:102 """Optimize VQE parameters."""103 def objective(params):104 # Simulate VQE energy calculation105 # In practice, this would involve quantum circuit simulation106 circuit_result = self._simulate_vqe_circuit(params, hamiltonian)107 return circuit_result108 109 # Use classical optimization110 result = minimize(objective, initial_params, method='BFGS')111 112 # Create optimal circuit113 optimal_circuit = QuantumCircuit(114 gates=[],115 parameters=result.x,116 num_qubits=int(np.log2(hamiltonian.shape[0])),117 depth=len(result.x) // 2118 )119 120 return {121 'ground_state_energy': result.fun,122 'optimization_success': result.success,123 'iterations': result.nit,124 'optimal_parameters': result.x,125 'optimal_circuit': optimal_circuit126 }127 128 def _optimize_qaoa(self, hamiltonian: np.ndarray, initial_params: np.ndarray) -> Dict[str, Any]:129 """Optimize QAOA parameters."""130 num_layers = len(initial_params) // 2131 132 def objective(params):133 beta = params[:num_layers]134 gamma = params[num_layers:]135 return self._simulate_qaoa_circuit(beta, gamma, hamiltonian)136 137 result = minimize(objective, initial_params, method='COBYLA')138 139 return {140 'optimal_value': -result.fun, # Minimize negative for maximization141 'optimization_success': result.success,142 'iterations': result.nit,143 'optimal_beta': result.x[:num_layers],144 'optimal_gamma': result.x[num_layers:]145 }146 147 def _simulate_vqe_circuit(self, params: np.ndarray, hamiltonian: np.ndarray) -> float:148 """Simulate VQE circuit and return energy expectation."""149 # Simplified simulation - create parameterized state150 num_qubits = int(np.log2(hamiltonian.shape[0]))151 152 # Create a parameterized quantum state (simplified)153 angles = params[:num_qubits]154 state = np.zeros(2**num_qubits, dtype=complex)155 156 # Simple parameterization: each qubit gets a rotation157 for i in range(2**num_qubits):158 amplitude = 1.0159 for q in range(num_qubits):160 if (i >> q) & 1:161 amplitude *= np.sin(angles[q % len(angles)])162 else:163 amplitude *= np.cos(angles[q % len(angles)])164 state[i] = amplitude165 166 # Normalize167 state = state / np.linalg.norm(state)168 169 # Calculate expectation value170 energy = np.real(np.conj(state).T @ hamiltonian @ state)171 return energy172 173 def _simulate_qaoa_circuit(self, beta: np.ndarray, gamma: np.ndarray, hamiltonian: np.ndarray) -> float:174 """Simulate QAOA circuit and return objective value."""175 # Simplified QAOA simulation176 num_qubits = int(np.log2(hamiltonian.shape[0]))177 178 # Start with uniform superposition179 state = np.ones(2**num_qubits, dtype=complex) / np.sqrt(2**num_qubits)180 181 # Apply QAOA layers (simplified)182 for i in range(len(beta)):183 # Problem Hamiltonian evolution (simplified)184 phase_factors = np.exp(-1j * gamma[i] * np.diag(hamiltonian))185 state = phase_factors * state186 187 # Mixer Hamiltonian evolution (simplified X rotations)188 # This is a very simplified version189 for q in range(num_qubits):190 # Apply rotation (simplified)191 rotation_factor = np.cos(beta[i]) + 1j * np.sin(beta[i])192 state = state * rotation_factor193 194 # Normalize195 state = state / np.linalg.norm(state)196 197 # Calculate expectation value198 expectation = np.real(np.conj(state).T @ hamiltonian @ state)199 return -expectation # Return negative for minimization200 201 def mitigate_errors(self, quantum_state: QuantumState, noise_model: Dict[str, Any]) -> QuantumState:202 """Apply AI-powered error mitigation."""203 logger.info("Applying error mitigation")204 205 # Initialize error mitigation network if not exists206 if self.error_mitigation_net is None:207 state_dim = len(quantum_state.amplitudes)208 self.error_mitigation_net = ErrorMitigationNetwork(state_dim)209 210 # Convert quantum state to real input (real and imaginary parts)211 state_real = np.real(quantum_state.amplitudes)212 state_imag = np.imag(quantum_state.amplitudes)213 input_data = np.concatenate([state_real, state_imag])214 215 # Apply noise simulation216 noise_factor = noise_model.get('noise_factor', 0.1)217 noisy_input = input_data + np.random.normal(0, noise_factor, input_data.shape)218 219 # Apply error mitigation (simplified - in practice would be trained)220 with torch.no_grad():221 input_tensor = torch.FloatTensor(noisy_input).unsqueeze(0)222 corrected_output = self.error_mitigation_net(input_tensor).squeeze(0).numpy()223 224 # Convert back to complex amplitudes225 mid_point = len(corrected_output) // 2226 corrected_real = corrected_output[:mid_point]227 corrected_imag = corrected_output[mid_point:]228 corrected_amplitudes = corrected_real + 1j * corrected_imag229 230 # Normalize231 corrected_amplitudes = corrected_amplitudes / np.linalg.norm(corrected_amplitudes)232 233 # Calculate improved fidelity234 original_fidelity = quantum_state.fidelity235 fidelity_improvement = min(0.1, noise_factor * 0.5) # Simplified improvement236 new_fidelity = min(1.0, original_fidelity + fidelity_improvement)237 238 return QuantumState(239 amplitudes=corrected_amplitudes,240 num_qubits=quantum_state.num_qubits,241 fidelity=new_fidelity242 )243 244 def optimize_resources(self, circuits: List[QuantumCircuit], available_qubits: int) -> Dict[str, Any]:245 """Optimize quantum resource allocation."""246 logger.info(f"Optimizing resources for {len(circuits)} circuits with {available_qubits} qubits")247 248 # Simple scheduling algorithm249 schedule = []250 current_time = 0251 total_qubits_used = 0252 253 # Sort circuits by qubit requirement (First-Fit Decreasing)254 sorted_circuits = sorted(enumerate(circuits), key=lambda x: x[1].num_qubits, reverse=True)255 256 for circuit_id, circuit in sorted_circuits:257 if circuit.num_qubits <= available_qubits:258 # Estimate execution time based on circuit depth259 estimated_duration = circuit.depth * 0.1 # 0.1 time units per gate260 261 schedule.append({262 'circuit_id': circuit_id,263 'qubits_allocated': circuit.num_qubits,264 'start_time': current_time,265 'estimated_duration': estimated_duration266 })267 268 current_time += estimated_duration269 total_qubits_used += circuit.num_qubits270 271 # Calculate resource utilization272 max_possible_qubits = len(circuits) * available_qubits273 resource_utilization = total_qubits_used / max_possible_qubits if max_possible_qubits > 0 else 0274 275 return {276 'schedule': schedule,277 'resource_utilization': resource_utilization,278 'estimated_runtime': current_time,279 'circuits_scheduled': len(schedule)280 }281 282 def hybrid_processing(self, classical_data: np.ndarray, quantum_component: str) -> Dict[str, Any]:283 """Perform hybrid quantum-classical processing."""284 logger.info(f"Running hybrid processing with {quantum_component}")285 286 # Preprocess classical data287 preprocessed_data = self._preprocess_classical_data(classical_data)288 289 # Apply quantum component290 if quantum_component == "quantum_kernel":291 quantum_result = self._apply_quantum_kernel(preprocessed_data)292 elif quantum_component == "quantum_feature_map":293 quantum_result = self._apply_quantum_feature_map(preprocessed_data)294 elif quantum_component == "quantum_neural_layer":295 quantum_result = self._apply_quantum_neural_layer(preprocessed_data)296 else:297 raise ValueError(f"Unknown quantum component: {quantum_component}")298 299 # Post-process results300 final_result = self._postprocess_quantum_result(quantum_result)301 302 return {303 'preprocessed_data': preprocessed_data,304 'quantum_result': quantum_result,305 'final_result': final_result306 }307 308 def _preprocess_classical_data(self, data: np.ndarray) -> np.ndarray:309 """Preprocess classical data for quantum processing."""310 # Normalize data311 normalized_data = (data - np.mean(data)) / (np.std(data) + 1e-8)312 313 # Apply some classical preprocessing314 processed_data = np.tanh(normalized_data) # Squash to [-1, 1]315 316 return processed_data317 318 def _apply_quantum_kernel(self, data: np.ndarray) -> np.ndarray:319 """Apply quantum kernel transformation."""320 # Simulate quantum kernel computation321 # In practice, this would involve quantum feature maps322 kernel_matrix = np.zeros((len(data), len(data)))323 324 for i in range(len(data)):325 for j in range(len(data)):326 # Simplified quantum kernel (RBF-like with quantum enhancement)327 diff = data[i] - data[j]328 quantum_enhancement = np.cos(np.pi * diff) * np.exp(-0.5 * diff**2)329 kernel_matrix[i, j] = quantum_enhancement330 331 return kernel_matrix332 333 def _apply_quantum_feature_map(self, data: np.ndarray) -> np.ndarray:334 """Apply quantum feature map."""335 # Simulate quantum feature mapping336 num_features = len(data)337 quantum_features = np.zeros(num_features * 2) # Expand feature space338 339 for i, x in enumerate(data):340 # Simulate quantum feature encoding341 quantum_features[2*i] = np.cos(np.pi * x)342 quantum_features[2*i + 1] = np.sin(np.pi * x)343 344 return quantum_features345 346 def _apply_quantum_neural_layer(self, data: np.ndarray) -> np.ndarray:347 """Apply quantum neural network layer."""348 # Simulate quantum neural network layer349 output_size = len(data)350 quantum_output = np.zeros(output_size)351 352 # Simplified quantum neural transformation353 for i, x in enumerate(data):354 # Simulate parameterized quantum circuit355 theta = x * np.pi / 4 # Parameter encoding356 quantum_output[i] = np.cos(theta) * np.exp(-0.1 * x**2)357 358 return quantum_output359 360 def _postprocess_quantum_result(self, quantum_result: np.ndarray) -> Dict[str, Any]:361 """Post-process quantum results."""362 # Calculate statistics363 stats = {364 'mean': np.mean(quantum_result),365 'std': np.std(quantum_result),366 'min': np.min(quantum_result),367 'max': np.max(quantum_result)368 }369 370 # Calculate confidence (simplified)371 confidence = 1.0 - np.std(quantum_result) / (np.abs(np.mean(quantum_result)) + 1e-8)372 confidence = max(0, min(1, confidence))373 374 return {375 'statistics': stats,376 'confidence': confidence,377 'processed_data': quantum_result378 }