Metadata-Version: 2.4
Name: qip-lumina
Version: 0.2.1
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-lumina
Project-URL: Documentation, https://github.com/Lumina-AI-Works/qip-lumina
Project-URL: Repository, https://github.com/Lumina-AI-Works/qip-lumina
Project-URL: Issues, https://github.com/Lumina-AI-Works/qip-lumina/issues
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 v0.2.1

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

[![PyPI version](https://img.shields.io/pypi/v/qip-lumina.svg)](https://pypi.org/project/qip-lumina/)
[![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 Gray Code encoding 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

```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/            # Experimental outputs & CSV files
└── docs/               # Technical documentation
```

---

## ▶️ Installation

```bash
pip install qip-lumina
```

---

## 🚀 Usage

### Run a Complete Experiment

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

### Single Image Processing

```bash
python inference.py path/to/image.png
```

### Generate Benchmark Dataset

```bash
python scripts/generate_research_data.py
```

---

## 📦 Features

- Hybrid Quantum–Classical Image Processing
- FRQI Image Encoding
- QPIE Image Encoding
- Quantum Edge Detection (QHED)
- Hardware-aware NISQ Noise Simulation
- Classical vs Quantum Benchmarking
- Research Dataset Generation
- CLI-based Experiment Pipeline

---

## 📚 Requirements

- Python 3.8+
- Qiskit
- Qiskit Aer
- NumPy
- Pandas
- OpenCV
- Matplotlib
- Scikit-image

---

## 📄 License

Licensed under the MIT License.

---

## 🏢 Developed By

**Lumina-AI-Works**

https://github.com/Lumina-AI-Works
