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Quantum Generative Adversarial Network

A quantum-enhanced generative adversarial network for high-dimensional data synthesis.

Why Use QuantumGAN?

Quantum-Enhanced Training

Leverage quantum computing principles to accelerate model training for complex data distributions.

High-Dimensional Data Support

Generate and manipulate high-dimensional data such as images, audio, and structured datasets.

Open Source & Extensible

Built with flexibility in mind—extend and customize the quantum integration layers as needed.

Scalable Architecture

Distribute computations across GPUs and quantum processors with built-in parallelism.

Hybrid Quantum-Classical

Seamlessly integrates quantum and classical layers for optimal performance.

Pre-Trained Models

Pre-trained models for common domains like images, speech, and text generation.

Getting Started with QuantumGAN

Install quantumgan via pip and choose a training mode.

pip install quantumgan
                    
  • Use quantumgan.train.classical() for classical backends.
  • Use quantumgan.train.quantum() for quantum processors (requires Qiskit or Cirq).
For detailed instructions and configuration options, refer to the full documentation. Read Docs →

Want to Dive Deeper?

View Full Documentation
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