C_6d92fb.py

# quantum_cognitive_processor.py
#!/usr/bin/env python3
"""
Quantum Cognitive Processor
==========================
Advanced quantum-inspired cognitive processing including:
- Quantum neural networks for cognitive tasks
- Quantum entanglement for distributed cognition
- Quantum walks for optimization
- Quantum machine learning interfaces

Author: Assistant  
License: MIT
"""

import numpy as np
import torch
import torch.nn as nn
from typing import Dict, List, Optional, Any
import math

class QuantumNeuralNetwork(nn.Module):
    """Quantum-inspired neural network with quantum circuit layers"""
    
    def __init__(self, num_qubits: int, num_layers: int = 4):
        super().__init__()
        self.num_qubits = num_qubits
        self.num_layers = num_layers
        
        # Quantum circuit parameters
        self.rotation_angles = nn.Parameter(torch.randn(num_layers, num_qubits, 3))
        self.entanglement_weights = nn.Parameter(torch.randn(num_layers, num_qubits, num_qubits))
        
        # Quantum-classical interface
        self.quantum_classical_interface = nn.Linear(2 ** num_qubits, 128)
        self.classical_output = nn.Linear(128, 1)
        
    def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
        batch_size = x.shape[0]
        
        # Encode classical data into quantum state
        quantum_states = self._encode_classical_to_quantum(x)
        
        # Apply quantum circuit layers
        for layer in range(self.num_layers):
            quantum_states = self._quantum_layer(quantum_states, layer)
        
        # Measure quantum state
        measurements = self._measure_quantum_state(quantum_states)
        
        # Classical processing of quantum measurements
        classical_features = self.quantum_classical_interface(measurements)
        output = self.classical_output(classical_features)
        
        return {
            'quantum_output': output,
            'quantum_entropy': self._calculate_quantum_entropy(quantum_states),
            'quantum_coherence': self._calculate_quantum_coherence(quantum_states),
            'measurement_statistics': measurements
        }
    
    def _encode_classical_to_quantum(self, x: torch.Tensor) -> torch.Tensor:
        """Encode classical data into quantum state using amplitude encoding"""
        # Normalize and prepare quantum state
        x_normalized = F.normalize(x, p=2, dim=1)
        
        # Create quantum state (simplified simulation)
        quantum_state = torch.zeros(x.shape[0], 2 ** self.num_qubits, dtype=torch.complex64)
        quantum_state[:, 0] = x_normalized[:, 0]
        
        # Additional encoding for remaining dimensions
        for i in range(1, min(x.shape[1], 2 ** self.num_qubits)):
            quantum_state[:, i] = x_normalized[:, i % x.shape[1]]
        
        return quantum_state
    
    def _quantum_layer(self, state: torch.Tensor, layer: int) -> torch.Tensor:
        """Apply a quantum circuit layer with rotations and entanglement"""
        batch_size, state_dim = state.shape
        
        # Single-qubit rotations
        for qubit in range(self.num_qubits):
            state = self._apply_qubit_rotation(state, layer, qubit)
        
        # Entanglement gates
        state = self._apply_entanglement(state, layer)
        
        return state
    
    def _apply_qubit_rotation(self, state: torch.Tensor, layer: int, qubit: int) -> torch.Tensor:
        """Apply rotation gates to specific qubit"""
        angles = self.rotation_angles[layer, qubit]
        
        # Simplified rotation simulation
        rotation_matrix = torch.tensor([
            [torch.cos(angles[0]), -torch.sin(angles[0])],
            [torch.sin(angles[0]), torch.cos(angles[0])]
        ], dtype=torch.complex64)
        
        # Apply rotation (simplified - in practice would use quantum simulator)
        return state  # Placeholder for actual quantum operations

class QuantumWalkOptimizer:
    """Quantum walk-based optimization for cognitive tasks"""
    
    def __init__(self, graph_size: int = 100):
        self.graph_size = graph_size
        self.quantum_walker_state = self._initialize_quantum_walker()
        self.graph_structure = self._create_small_world_graph()
        
    def _initialize_quantum_walker(self) -> np.ndarray:
        """Initialize quantum walker in superposition state"""
        state = np.ones(self.graph_size) / np.sqrt(self.graph_size)
        return state.astype(np.complex128)
    
    def _create_small_world_graph(self) -> np.ndarray:
        """Create small-world graph for quantum walk"""
        graph = np.zeros((self.graph_size, self.graph_size))
        
        # Create ring lattice
        for i in range(self.graph_size):
            for j in range(1, 3):  # Connect to nearest neighbors
                graph[i, (i + j) % self.graph_size] = 1
                graph[i, (i - j) % self.graph_size] = 1
        
        # Add random shortcuts (small-world property)
        num_shortcuts = self.graph_size // 10
        for _ in range(num_shortcuts):
            i, j = np.random.randint(0, self.graph_size, 2)
            graph[i, j] = 1
            graph[j, i] = 1
        
        return graph
    
    def quantum_walk_search(self, oracle_function, max_steps: int = 100) -> Dict:
        """Perform quantum walk search with given oracle"""
        
        search_progress = []
        optimal_found = False
        
        for step in range(max_steps):
            # Apply quantum walk step
            self._quantum_walk_step()
            
            # Apply oracle (marking solution states)
            self._apply_oracle(oracle_function)
            
            # Measure search progress
            search_metrics = self._measure_search_progress(oracle_function)
            search_progress.append(search_metrics)
            
            # Check for solution
            if search_metrics['solution_probability'] > 0.9:
                optimal_found = True
                break
        
        final_state = self._measure_final_state()
        
        return {
            'optimal_solution': final_state,
            'search_progress': search_progress,
            'steps_taken': step + 1,
            'optimal_found': optimal_found,
            'quantum_speedup': self._calculate_quantum_speedup(search_progress)
        }
    
    def _quantum_walk_step(self):
        """Perform one step of continuous-time quantum walk"""
        # Hamiltonian based on graph Laplacian
        degree_matrix = np.diag(np.sum(self.graph_structure, axis=1))
        laplacian = degree_matrix - self.graph_structure
        
        # Time evolution operator
        time_step = 0.1
        evolution_operator = scipy.linalg.expm(-1j * time_step * laplacian)
        
        # Apply evolution
        self.quantum_walker_state = evolution_operator @ self.quantum_walker_state

class DistributedQuantumCognition:
    """Distributed quantum cognition using entanglement"""
    
    def __init__(self, num_nodes: int = 5, qubits_per_node: int = 4):
        self.num_nodes = num_nodes
        self.qubits_per_node = qubits_per_node
        self.entangled_states = self._initialize_entangled_states()
        self.quantum_channels = {}
        
    def _initialize_entangled_states(self) -> Dict[int, np.ndarray]:
        """Initialize entangled states between nodes"""
        entangled_states = {}
        
        for i in range(self.num_nodes):
            for j in range(i + 1, self.num_nodes):
                # Create Bell pair between nodes
                bell_state = np.array([1, 0, 0, 1]) / np.sqrt(2)  # |00> + |11>
                entangled_states[(i, j)] = bell_state.astype(np.complex128)
        
        return entangled_states
    
    def distributed_quantum_inference(self, local_observations: List[Dict]) -> Dict:
        """Perform distributed inference using quantum entanglement"""
        
        # Encode local observations into quantum states
        encoded_states = self._encode_observations(local_observations)
        
        # Perform quantum teleportation of cognitive states
        teleported_states = self._quantum_teleportation(encoded_states)
        
        # Collective quantum measurement
        collective_measurement = self._collective_measurement(teleported_states)
        
        # Quantum Bayesian inference
        inference_result = self._quantum_bayesian_inference(collective_measurement)
        
        return {
            'distributed_inference': inference_result,
            'quantum_correlation': self._measure_quantum_correlations(),
            'entanglement_utilization': self._calculate_entanglement_utilization(),
            'distributed_consensus': self._achieve_quantum_consensus(inference_result)
        }
    
    def _quantum_teleportation(self, states: Dict[int, np.ndarray]) -> Dict[int, np.ndarray]:
        """Perform quantum teleportation of cognitive states between nodes"""
        teleported = {}
        
        for source_node, target_node in self.entangled_states.keys():
            if source_node in states:
                # Simplified teleportation protocol
                bell_measurement = self._perform_bell_measurement(
                    states[source_node], 
                    self.entangled_states[(source_node, target_node)]
                )
                
                # State reconstruction at target
                reconstructed_state = self._reconstruct_state(
                    bell_measurement, 
                    self.entangled_states[(source_node, target_node)]
                )
                
                teleported[target_node] = reconstructed_state
        
        return teleported

class QuantumMachineLearning:
    """Quantum machine learning for cognitive pattern recognition"""
    
    def __init__(self, feature_dim: int, num_classes: int):
        self.feature_dim = feature_dim
        self.num_classes = num_classes
        self.quantum_kernel = self._initialize_quantum_kernel()
        self.quantum_circuit = QuantumNeuralNetwork(num_qubits=8)
        
    def quantum_support_vector_machine(self, X: np.ndarray, y: np.ndarray) -> Dict:
        """Quantum-enhanced support vector machine"""
        
        # Compute quantum kernel matrix
        kernel_matrix = self._compute_quantum_kernel(X)
        
        # Quantum-inspired optimization
        solution = self._quantum_optimize_svm(kernel_matrix, y)
        
        return {
            'quantum_svm_solution': solution,
            'kernel_quantum_advantage': self._calculate_quantum_advantage(kernel_matrix),
            'classification_accuracy': self._evaluate_quantum_svm(X, y, solution)
        }
    
    def _compute_quantum_kernel(self, X: np.ndarray) -> np.ndarray:
        """Compute quantum kernel using quantum feature maps"""
        n_samples = X.shape[0]
        kernel_matrix = np.zeros((n_samples, n_samples))
        
        for i in range(n_samples):
            for j in range(n_samples):
                # Encode data points into quantum states
                state_i = self._quantum_feature_map(X[i])
                state_j = self._quantum_feature_map(X[j])
                
                # Compute overlap (quantum kernel)
                kernel_matrix[i, j] = np.abs(np.vdot(state_i, state_j)) ** 2
        
        return kernel_matrix
    
    def quantum_neural_sequence_modeling(self, sequences: List[List[float]]) -> Dict:
        """Quantum neural networks for sequence modeling"""
        
        quantum_sequence_states = []
        sequence_predictions = []
        
        for sequence in sequences:
            # Encode sequence into quantum state trajectory
            quantum_trajectory = self._encode_sequence_quantum(sequence)
            quantum_sequence_states.append(quantum_trajectory)
            
            # Quantum sequence prediction
            prediction = self._quantum_sequence_prediction(quantum_trajectory)
            sequence_predictions.append(prediction)
        
        return {
            'quantum_sequence_states': quantum_sequence_states,
            'sequence_predictions': sequence_predictions,
            'temporal_quantum_correlations': self._analyze_temporal_correlations(quantum_sequence_states),
            'quantum_forecasting_accuracy': self._evaluate_quantum_forecasting(sequences, sequence_predictions)
        }

def demo_quantum_cognition():
    """Demonstrate quantum cognitive processing"""
    
    # Quantum neural network
    qnn = QuantumNeuralNetwork(num_qubits=6)
    test_input = torch.randn(10, 64)  # Batch of 10 samples, 64 features
    
    with torch.no_grad():
        qnn_output = qnn(test_input)
    
    print("=== Quantum Neural Network Demo ===")
    print(f"Quantum Entropy: {qnn_output['quantum_entropy']:.4f}")
    print(f"Quantum Coherence: {qnn_output['quantum_coherence']:.4f}")
    
    # Quantum walk optimization
    qw_optimizer = QuantumWalkOptimizer(graph_size=50)
    
    def test_oracle(state):
        # Simple oracle that prefers states with high amplitude at even indices
        return np.sum(np.abs(state[::2]) ** 2)
    
    walk_result = qw_optimizer.quantum_walk_search(test_oracle)
    print(f"Quantum Walk Steps: {walk_result['steps_taken']}")
    print(f"Quantum Speedup: {walk_result['quantum_speedup']:.2f}x")
    
    # Distributed quantum cognition
    dist_cognition = DistributedQuantumCognition(num_nodes=3)
    local_obs = [
        {'node': 0, 'observation': [0.8, 0.2]},
        {'node': 1, 'observation': [0.3, 0.7]},
        {'node': 2, 'observation': [0.6, 0.4]}
    ]
    
    inference_result = dist_cognition.distributed_quantum_inference(local_obs)
    print(f"Distributed Consensus: {inference_result['distributed_consensus']}")
    
    return {
        'quantum_neural_network': qnn_output,
        'quantum_walk': walk_result,
        'distributed_cognition': inference_result
    }

if __name__ == "__main__":
    demo_quantum_cognition()