🌌 Quantum Image Processing Framework

📝 Overview

A high-performance Quantum Image Processing (QIP) application built to bridge quantum computing simulation primitives with traditional computer vision workflows. The core framework implements hybrid quantum-classical algorithms for parallelized, accelerated image manipulation, designed with a concurrent backend to handle multi-threaded processing tasks.

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🚀 Key Features

🧮 Quantum Algorithms

• Quantum Edge Detection: Uses quantum superposition and entanglement principles to identify spatial boundaries.
• Quantum Image Compression: Utilizes compact quantum state preparation (such as Novel Enhanced Quantum Representation / NEQR mapping) to achieve a theoretical 75% structural size reduction.
• Quantum Feature Extraction: Employs Hadamard gates and controlled rotations ($CR_y$) to extract high-dimensional image features.
• Quantum Fourier Transform (QFT): Executes localized frequency domain analysis directly within simulated quantum circuits.

⚡ Concurrent Core Engine

• Multi-Threaded Pipeline: Asynchronous backend processing engineered to handle multiple image processing requests simultaneously without thread-blocking.
• Queue Management: Robust task queuing to maintain operational health under high-throughput request spikes.
• Live Analytics & Diagnostics: Exposes metrics endpoints to monitor execution performance, system resource consumption, and processing speed (~2,000 pixels/second).

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🛠️ Tech Stack & Prerequisites

• Languages & Core: Python 3.11+
• Quantum Simulation: Qiskit / PennyLane
• Backend Architecture: Flask / FastAPI
• Matrix Operations: NumPy, Pillow (PIL)

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💻 Local Development

1. Clone & Navigate

git clone [https://github.com/Komal-phogat/quantum-image-processor.git](https://github.com/Komal-phogat/quantum-image-processor.git)
cd quantum-image-processor

Setup

# Create and activate a virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate

# Install required dependencies
pip install -r requirements.txt

# Run locally
python app.py

API Documentation

Method	Endpoint	Description
GET	/	Web interface for testing
POST	/api/process	Submit image for quantum processing
GET	/api/result/{id}	Get processing result
GET	/api/stats	Get server statistics

License

MIT License - see LICENSE file for details.