quantum-xai 0.1.3

Explainable Quantum Machine Learning Library for interpretable quantum models

Quantum-XAI Library

Quantum-XAI is a comprehensive explainable quantum machine learning library designed to provide interpretability and transparency for quantum neural network models. Inspired by classical explainability tools like SHAP and LIME, Quantum-XAI adapts these concepts to the quantum domain, enabling users to understand and trust quantum model predictions.

Features

• Variational Quantum Classifier (VQC) implementation with flexible encoding schemes.
• Multiple quantum explainability methods including Quantum SHAP, Gradient, LIME, and Perturbation explainers.
• Visualization tools for feature importance and quantum circuit explanations.
• Dataset loaders for popular datasets prepared for quantum machine learning.
• Benchmarking and evaluation utilities for explanation quality and consistency.
• Comprehensive documentation, examples, and community standards.

Installation

Install via pip from PyPI:

pip install quantum_xai

Or clone the repository and install dependencies:

git clone https://github.com/Harsha2318/QUANTUM-XAI-library.git
cd QUANTUM-XAI-library
pip install -r requirements.txt

Usage

Quick Start Example

from quantum_xai import QuantumXAIDemo

demo = QuantumXAIDemo()
results = demo.run_complete_demo(dataset='iris', n_samples=80)

Custom Model Training

from quantum_xai import QuantumNeuralNetwork, QuantumSHAPExplainer, QuantumGradientExplainer, QuantumXAIVisualizer
from sklearn.model_selection import train_test_split
from quantum_xai import QuantumDatasetLoader

# Load data
X, y, feature_names = QuantumDatasetLoader.load_iris_quantum(n_samples=100)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Create and train model
model = QuantumNeuralNetwork(n_features=X.shape[1], n_qubits=4, n_layers=2)
model.train(X_train, y_train, epochs=100, lr=0.1)

# Create explainers
shap_explainer = QuantumSHAPExplainer(model, X_train)
gradient_explainer = QuantumGradientExplainer(model)

# Generate explanation for a sample
explanation = shap_explainer.explain(X_test, 0)

# Visualize explanation
visualizer = QuantumXAIVisualizer()
fig = visualizer.plot_feature_importance(explanation, feature_names)
fig.show()

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Research Applications

• Benchmark quantum vs classical explainability methods
• Analyze quantum Fisher information and entanglement effects
• Extend to other quantum platforms (Qiskit, Cirq)
• Develop advanced quantum-specific explanation metrics
• Apply to real quantum datasets in chemistry, finance, and more

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Project Structure

• QuantumNeuralNetwork: Variational quantum classifier model
• QuantumExplainer and subclasses: Explainability methods (SHAP, Gradient, LIME, Perturbation)
• QuantumXAIVisualizer: Visualization utilities
• QuantumDatasetLoader: Dataset loading and preprocessing
• QuantumXAIBenchmark: Benchmarking and evaluation tools
• QuantumXAIDemo: Complete demo and example workflows
• save_model_and_explanations / load_model_and_explanations: Persistence utilities
• QuantumXAIResearch: Advanced research features

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License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

Developed by Quantum-XAI Contributors.

Contact

For questions or support, please open an issue on GitHub.