qifeaturex 0.1.1

Quantum Information Feature Engineering Library

QIFeatureX 🔮

Quantum Information Feature Engineering Library

QIFeatureX is an open-source Quantum Information Feature Engineering library that converts quantum states (pure vectors or density matrices) into machine-learning-ready numerical feature vectors. It enables ML-driven analysis of entanglement, coherence, entropy, nonlocality, and quantum similarity without heavy symbolic calculations.

QIFeatureX is designed for research in quantum computing, quantum communication, quantum sensing, quantum machine learning, and condensed matter physics.

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✨ Key Features

• 📌 Convert quantum states → structured ML feature tables
• 📌 Support for pure states (|ψ⟩) and density matrices (ρ)
• 📌 Entanglement metrics: concurrence, negativity, log-negativity, tangle
• 📌 Entropy metrics: von Neumann, Rényi-2, linear entropy
• 📌 Coherence measures: ℓ₁-coherence, relative entropy of coherence
• 📌 Mutual information & bipartite correlations
• 📌 Bell-CHSH violation measurement
• 📌 Quantum similarity distances: trace distance, fidelity, Bures, Hilbert-Schmidt
• 📌 Fully compatible with scikit-learn pipelines

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🚀 Installation

pip install qifeaturex

### 2. Right below that section, paste the Basic Usage example block:

```markdown
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## 🧠 Basic Usage Example

```python
import numpy as np
from qifeaturex import extract_features
from qifeaturex.ml import QIFeatureExtractor
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline

def bell_state_phi_plus():
    psi = np.zeros(4, dtype=complex)
    psi[0] = psi[3] = 1/np.sqrt(2)
    return psi

# Create a Bell state and convert to density matrix
psi = bell_state_phi_plus()
rho = np.outer(psi, psi.conj())

# Extract features
df = extract_features([rho], dims=(2,2))
print(df)