qip-lumina 0.2.0

A research-grade Hybrid Quantum-Classical Image Processing Framework with NISQ noise simulation.

QIP Framework v0.2.0

Hybrid Quantum–Classical Image Processing Framework for NISQ-Era Analysis

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🔬 Research Context

The Problem

Quantum Image Processing (QIP) promises exponential advantages in computational complexity and storage. However, current hardware noise (decoherence, gate errors) poses a significant bottleneck. This framework provides the tools to analyze these trade-offs through hardware-aware simulation and rigorous benchmarking.

Our Solution

We present a modular framework designed to analyze these trade-offs. By implementing optimized encoding (Gray Code) and realistic hardware-aware noise models, this framework allows researchers to benchmark quantum algorithms against classical baselines under realistic constraints.

👥 Authors

• M. Sowmya Priya
• K. Lalitha

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🛠️ Framework Architecture

The project follows a "System" design pattern similar to professional research repos:

qip_project/
├── qip_framework/      # Core Library Logic
├── main.py             # System entry point (CLI)
├── inference.py        # Single-image processing script
├── scripts/            # Benchmarking & Data generation tools
├── data/               # Sample research datasets
├── results/            # Proof-of-results & Data CSVs
└── docs/               # Technical theory guides

▶️ Usage Clarity

Installation

pip install qip-framework

1. Main Entry Point (CLI)

Run a full experiment using the command-line interface:

python main.py --algo qhed --encoding qpie --image data/sample.png

2. Single-Image Inference

Process any image quickly:

python inference.py path/to/image.png

3. Research Benchmarking

Generate the scientific dataset used in our research:

python scripts/generate_research_data.py

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Developed at Lumina-AI-Works.