QU-TRAX Algorithm - Quantum-Inspired Logistics Optimizer

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Project Overview

QU-TRAX is a production-ready, quantum-inspired logistics optimization engine designed to solve complex problems in supply chain management, such as:

• Route optimization (TSP, VRP)
• Dynamic scheduling and resource allocation
• Intelligent warehouse and traffic load management

Leveraging quantum-inspired algorithms like Simulated Annealing and Quantum Collapse techniques, Q-TRAX aims to provide efficient, scalable, and flexible solutions to logistics challenges.

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🛠️ Features Developed Till Date

• Modular backend architecture built with Python FastAPI and async workers
• Dynamic traffic generator module simulating logistics loads and scenarios
• Redis-based job queue with RQ workers for asynchronous task execution
• WebSocket support for real-time progress updates on optimization jobs
• Configurable quantum-inspired simulation algorithm core
• Support for multiple environment setups (Mac, Windows, Linux) with detailed installation scripts
• Logging and debugging facilities to trace optimization workflow and worker statuses

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⚙️ Technology Stack

Component	Technology / Tool
Backend Framework	FastAPI (Python)
Task Queue & Workers	Redis + RQ
Algorithm Core	Custom Quantum-inspired Simulation (Python)
Real-time Updates	WebSockets
Database (planned)	PostgreSQL (pgvector for embeddings)
Containerization	Docker (planned)
Development OS	MacOS, Windows, Linux

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📥 Installation & Setup

Prerequisites

• Python 3.10+
• Redis server
• Git
• (Optional) Docker

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1. Clone the repository

git clone https://github.com/BUILDING-ADOCY/Q-TRAX-ALGORITHM.git
cd q-trax

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2. Setup Python virtual environment & install dependencies

Mac & Linux:

python3 -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt

Windows (PowerShell):

python -m venv venv
.\venv\Scripts\Activate.ps1
pip install --upgrade pip
pip install -r requirements.txt

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3. Install and start Redis server

Mac (with Homebrew):

brew install redis
brew services start redis

Verify Redis is running:

redis-cli ping
# Should respond with PONG

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Windows:

• Download Redis for Windows from https://github.com/tporadowski/redis/releases
• Extract and run redis-server.exe manually or install as a service.
• To start manually (in Command Prompt or PowerShell):

redis-server

Verify Redis is running in another terminal:

redis-cli ping
# Should respond with PONG

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Linux (Ubuntu/Debian):

sudo apt update
sudo apt install redis-server
sudo systemctl start redis
sudo systemctl enable redis

Verify Redis is running:

redis-cli ping
# Should respond with PONG

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4. Run RQ Worker to process optimization jobs

In a new terminal window, activate your virtual environment and run:

rq worker qtrax

You should see the worker waiting for jobs.

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5. Start the FastAPI backend server

Run:

uvicorn main:app --reload

The API will be available at: http://localhost:8000

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🧩 Basic Usage

• Submit optimization jobs via the API endpoint /enqueue_dynamic
• Track job status and progress in real-time using WebSocket /ws/results/{job_id}
• The backend asynchronously executes the quantum-inspired simulation algorithm and streams updates until completion

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📄 Technical Documentation & Use Case

Use Case: Dynamic Route Optimization

Scenario: A logistics company wants to optimize delivery routes for a fleet of vehicles, minimizing total travel distance while factoring in traffic and warehouse constraints.

How Q-TRAX Works:

• The client submits delivery points and constraints to Q-TRAX backend.
• Q-TRAX’s quantum-inspired algorithm uses simulated annealing and quantum collapse heuristics to explore the solution space.
• The process runs asynchronously, providing real-time progress updates.
• The optimized routing solution is returned for execution, improving logistics efficiency.

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🔮 Future Roadmap

• Integrate PostgreSQL database with pgvector for embedding-based search
• Dockerize components for container-based deployment
• Build a frontend dashboard with interactive visualizations
• Add machine learning-based traffic and demand forecasting
• Extend support for multimodal transport and advanced resource scheduling

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📚 References & Resources

• FastAPI Documentation
• Redis & RQ Documentation
• Quantum-inspired optimization algorithms research papers (available on request)

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📝 Contact & Contribution

Feel free to contribute or raise issues! Contact: [surajmahapatra2003@gmail.com] GitHub: (https://github.com/BUILDING-ADOCY/Q-TRAX-ALGORITHM.git)

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