Metadata-Version: 2.4
Name: qip-lumina
Version: 0.2.0
Summary: A research-grade Hybrid Quantum-Classical Image Processing Framework with NISQ noise simulation.
Author-email: "M. Sowmya Priya" <sowmyapriya7325@gmail.com>, "K. Lalitha" <lalitha.koruprolu29@gmail.com>
License: MIT
Project-URL: Homepage, https://github.com/Lumina-AI-Works/qip-framework
Project-URL: Documentation, https://Lumina-AI-Works.github.io/qip-framework/
Project-URL: Bug Tracker, https://github.com/Lumina-AI-Works/qip-framework/issues
Project-URL: Source Code, https://github.com/Lumina-AI-Works/qip-framework
Keywords: quantum,qiskit,image-processing,nisq,qhed,frqi,qpie
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: qiskit>=0.45.0
Requires-Dist: qiskit-aer>=0.13.0
Requires-Dist: opencv-python>=4.8.0
Requires-Dist: matplotlib>=3.7.0
Requires-Dist: numpy>=1.24.0
Requires-Dist: pandas>=2.0.0
Requires-Dist: tabulate>=0.9.0
Requires-Dist: scikit-image>=0.21.0
Dynamic: license-file

# QIP Framework v0.2.0

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

[![PyPI version](https://img.shields.io/pypi/v/qip-framework.svg)](https://pypi.org/project/qip-framework/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

---

## 🔬 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](https://github.com/Sowmyapriya-Mahamkali)**
- **[K. Lalitha](https://github.com/Lalitha-Koruprolu)**

---

## 🛠️ Framework Architecture
The project follows a "System" design pattern similar to professional research repos:

```text
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
```bash
pip install qip-framework
```

### 1. Main Entry Point (CLI)
Run a full experiment using the command-line interface:
```bash
python main.py --algo qhed --encoding qpie --image data/sample.png
```

### 2. Single-Image Inference
Process any image quickly:
```bash
python inference.py path/to/image.png
```

### 3. Research Benchmarking
Generate the scientific dataset used in our research:
```bash
python scripts/generate_research_data.py
```

---

Developed at **[Lumina-AI-Works](https://github.com/Lumina-AI-Works)**.
