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Installation Guide

This guide will walk you through installing QGANS Pro and its dependencies.

Requirements

  • Python 3.8 or higher
  • pip package manager
  • (Optional) CUDA-capable GPU for accelerated training

Basic Installation

pip install quantum-generative-adversarial-networks-pro

From Source

git clone https://github.com/krish567366/quantum-generative-adversarial-networks-pro.git
cd quantum-generative-adversarial-networks-pro
pip install -e .

Development Installation

For development or contributing to the project:

git clone https://github.com/krish567366/quantum-generative-adversarial-networks-pro.git
cd quantum-generative-adversarial-networks-pro
pip install -e ".[dev,docs,jupyter]"

Optional Dependencies

Quantum Computing Backends

Qiskit (IBM)

pip install qiskit qiskit-aer qiskit-ibmq-provider

PennyLane (Xanadu)

pip install pennylane pennylane-lightning

Machine Learning Frameworks

The package requires PyTorch, which should be installed automatically. For GPU support:

# CUDA 11.8
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

# CUDA 12.1
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121

# CPU only
pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu

Visualization and Jupyter

pip install matplotlib seaborn plotly
pip install jupyter ipywidgets

Verification

Test your installation:

import qgans_pro
from qgans_pro import QuantumGenerator, QuantumDiscriminator

print(f"QGANS Pro version: {qgans_pro.__version__}")

# Test quantum backend
try:
    generator = QuantumGenerator(n_qubits=4, n_layers=2, output_dim=10)
    print("✅ Quantum components working correctly")
except Exception as e:
    print(f"❌ Error: {e}")

Docker Installation

Pull Pre-built Image

docker pull krishnabajpai/qgans-pro:latest
docker run -it krishnabajpai/qgans-pro:latest

Build from Source

git clone https://github.com/krish567366/quantum-generative-adversarial-networks-pro.git
cd quantum-generative-adversarial-networks-pro
docker build -t qgans-pro .
docker run -it qgans-pro

Common Issues

Issue: ImportError for quantum backends

Solution: Install the specific quantum backend you want to use:

# For Qiskit
pip install qiskit

# For PennyLane  
pip install pennylane

Issue: CUDA out of memory

Solution: Reduce batch size or use CPU:

# Use smaller batch size
data_loader = get_data_loader("mnist", batch_size=32)

# Force CPU usage
device = torch.device("cpu")

Issue: Slow training on CPU

Solution: Install GPU-enabled PyTorch or use quantum simulators with acceleration:

# Install CUDA PyTorch
pip install torch --index-url https://download.pytorch.org/whl/cu118

# Use optimized quantum simulators
pip install qiskit-aer[gpu]  # GPU-accelerated Qiskit

Performance Optimization

For Classical Training

  • Use GPU-enabled PyTorch
  • Increase batch size if memory allows
  • Use multiple CPU cores: num_workers=4 in DataLoader

For Quantum Training

  • Use optimized quantum simulators
  • Consider using real quantum hardware for small circuits
  • Enable quantum circuit optimization in backends

Memory Management

# Clear GPU cache periodically
import torch
torch.cuda.empty_cache()

# Use gradient checkpointing for large models
# (Implementation depends on your specific use case)

Next Steps

After installation, check out:

  1. Quick Start Guide - Train your first quantum GAN
  2. Configuration Guide - Customize your setup
  3. Examples - Jupyter notebook tutorials