Create quantum_agent.py

quantum_agent.py

@@ -0,0 +1,378 @@
+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
+        }