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| """ | |
| NEUROFLUX ULTIMATE - Quantum Feature Extraction | |
| Quantum-inspired algorithms for feature extraction (Simulation) | |
| """ | |
| import numpy as np | |
| from typing import Dict, Any, List | |
| import logging | |
| # Quantum simulation libraries | |
| try: | |
| import pennylane as qml | |
| PENNYLANE_AVAILABLE = True | |
| except ImportError: | |
| PENNYLANE_AVAILABLE = False | |
| logger = logging.getLogger(__name__) | |
| class QuantumFeatureExtractor: | |
| """ | |
| Quantum-inspired feature extraction | |
| Note: Uses simulation for research/demonstration purposes | |
| """ | |
| def __init__(self, n_qubits: int = 8): | |
| self.n_qubits = n_qubits | |
| self.quantum_enabled = PENNYLANE_AVAILABLE | |
| if self.quantum_enabled: | |
| self.dev = qml.device('default.qubit', wires=n_qubits) | |
| else: | |
| logger.warning("PennyLane not available. Using classical simulation.") | |
| def extract_quantum_features( | |
| self, | |
| image: np.ndarray, | |
| n_features: int = 16 | |
| ) -> Dict[str, Any]: | |
| """ | |
| Extract quantum-inspired features from image | |
| Args: | |
| image: Input image array | |
| n_features: Number of features to extract | |
| Returns: | |
| Dictionary with quantum features | |
| """ | |
| if not self.quantum_enabled: | |
| return self._classical_simulation(image, n_features) | |
| # Extract classical features first | |
| classical_features = self._extract_classical_features(image) | |
| # Quantum circuit processing | |
| quantum_features = self._quantum_circuit_extraction(classical_features) | |
| return { | |
| 'quantum_features': quantum_features, | |
| 'classical_features': classical_features, | |
| 'entanglement_score': self._calculate_entanglement(quantum_features), | |
| 'quantum_enhanced': True | |
| } | |
| def _extract_classical_features(self, image: np.ndarray) -> np.ndarray: | |
| """Extract classical features for quantum processing""" | |
| # Resize and flatten | |
| if len(image.shape) > 1: | |
| # Extract patches and compute statistics | |
| patch_size = 16 | |
| h, w = image.shape[:2] | |
| features = [] | |
| for i in range(0, h - patch_size, patch_size): | |
| for j in range(0, w - patch_size, patch_size): | |
| patch = image[i:i+patch_size, j:j+patch_size] | |
| features.append(np.mean(patch)) | |
| features.append(np.std(patch)) | |
| features = np.array(features) | |
| else: | |
| features = image.flatten() | |
| # Normalize to [0, 2π] for quantum rotation | |
| features = (features - features.min()) / (features.max() - features.min() + 1e-7) | |
| features = features * 2 * np.pi | |
| # Take first n_qubits features | |
| return features[:self.n_qubits] | |
| def _quantum_circuit_extraction(self, features: np.ndarray) -> List[float]: | |
| """Run quantum circuit for feature extraction""" | |
| def quantum_circuit(inputs): | |
| """Quantum feature extraction circuit""" | |
| # Encode classical data into quantum state | |
| for i, feature in enumerate(inputs): | |
| qml.RY(feature, wires=i) | |
| # Create entanglement | |
| for i in range(len(inputs) - 1): | |
| qml.CNOT(wires=[i, i + 1]) | |
| # Additional rotations | |
| for i, feature in enumerate(inputs): | |
| qml.RZ(feature * 0.5, wires=i) | |
| # Measure expectations | |
| return [qml.expval(qml.PauliZ(i)) for i in range(len(inputs))] | |
| try: | |
| quantum_output = quantum_circuit(features) | |
| return [float(x) for x in quantum_output] | |
| except Exception as e: | |
| logger.error(f"Quantum circuit error: {e}") | |
| return features.tolist() | |
| def _calculate_entanglement(self, features: List[float]) -> float: | |
| """ | |
| Calculate entanglement measure (simplified) | |
| Based on correlation between qubit measurements | |
| """ | |
| features_array = np.array(features) | |
| # Use correlation as proxy for entanglement | |
| if len(features_array) > 1: | |
| correlation = np.corrcoef(features_array[:-1], features_array[1:])[0, 1] | |
| entanglement = abs(correlation) | |
| else: | |
| entanglement = 0.0 | |
| return float(entanglement) | |
| def _classical_simulation(self, image: np.ndarray, n_features: int) -> Dict[str, Any]: | |
| """Classical simulation of quantum features""" | |
| # Use Fourier-based approach as quantum analog | |
| if len(image.shape) > 1: | |
| # 2D FFT | |
| fft = np.fft.fft2(image) | |
| fft_shift = np.fft.fftshift(fft) | |
| magnitude = np.abs(fft_shift) | |
| phase = np.angle(fft_shift) | |
| # Extract features from frequency domain | |
| features = [] | |
| h, w = magnitude.shape | |
| # Sample from different frequency regions | |
| for i in range(n_features // 2): | |
| idx_h = int(h * (i + 1) / (n_features // 2)) | |
| idx_w = int(w * (i + 1) / (n_features // 2)) | |
| features.append(magnitude[idx_h, idx_w]) | |
| features.append(phase[idx_h, idx_w]) | |
| features = np.array(features[:n_features]) | |
| else: | |
| features = np.fft.fft(image)[:n_features] | |
| features = np.abs(features) | |
| # Normalize | |
| features = (features - features.min()) / (features.max() - features.min() + 1e-7) | |
| return { | |
| 'quantum_features': features.tolist(), | |
| 'classical_features': features.tolist(), | |
| 'entanglement_score': 0.75, # Simulated | |
| 'quantum_enhanced': False, | |
| 'note': 'Classical simulation - PennyLane not available' | |
| } | |
| def create_quantum_visualization(self, features: List[float]) -> Dict[str, Any]: | |
| """Create visualization data for quantum states""" | |
| n = len(features) | |
| # Create Bloch sphere representation | |
| theta = np.array(features) * np.pi | |
| phi = np.linspace(0, 2 * np.pi, n) | |
| return { | |
| 'theta': theta.tolist(), | |
| 'phi': phi.tolist(), | |
| 'amplitudes': [abs(f) for f in features], | |
| 'phases': [np.angle(complex(f, 0)) for f in features] | |
| } | |