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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"""
@qml.qnode(self.dev)
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]
}
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