🚀 Hybrid Classical–Quantum Path Planning System

📌 Overview

This project implements a Hybrid AI Path Planning System that combines:

• 🧠 Classical algorithm (A*)
• ⚛️ Quantum-inspired probabilistic approach
• 🤖 LLM-based reasoning (Google Gemini)
• 📊 Data analysis & visualization
• 📄 Automated PDF report generation
• 🌐 Interactive UI using Streamlit
• 🔁 CI/CD using GitHub Actions

The system operates in a 2D grid environment with obstacles, finding optimal paths and comparing deterministic vs probabilistic decision-making.

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🎯 Objectives

• Implement efficient path planning using A*
• Explore quantum-inspired probabilistic navigation
• Compare both approaches using metrics
• Integrate LLM-based explanations
• Automate analysis + reporting pipeline
• Deploy an interactive UI for demonstration

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🧠 Classical Approach (A*)

• Uses heuristic-based search
• Guarantees shortest path (if exists)

Formula:

f(n) = g(n) + h(n)

• g(n): Cost from start
• h(n): Manhattan distance

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⚛️ Quantum-Inspired Approach

• Inspired by Grover-like probabilistic selection
• Chooses among optimal moves randomly

Characteristics:

• Probabilistic decisions
• Non-deterministic output
• Varies across runs

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⚖️ Comparison

Feature	A*	Quantum
Type	Deterministic	Probabilistic
Optimal Path	✅ Guaranteed	❌ Not guaranteed
Reliability	High	Medium
Behavior	Consistent	Variable

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📊 System Architecture

Quantum-ai-path-planning/
│
├── classical/                # A* Algorithm
├── quantum/                  # Quantum-inspired logic
├── LLM Explanation/          # AI + Analytics pipeline
│   ├── app.py
│   ├── metrics.py
│   ├── visualization.py
│   ├── llm_module.py
│   ├── pdf_report.py
│   └── requirements.txt
│
├── app_ui.py                 # Streamlit UI
├── test_astar.py             # Unit tests
├── .github/workflows/ci.yml  # CI/CD pipeline
└── README.md

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⚙️ Features

✅ Path Planning

• A* shortest path algorithm
• Quantum-inspired probabilistic navigation

📊 Analytics

• Success rate
• Path length
• Explored nodes

🤖 AI Integration

• LLM-based explanation using Google Gemini
• Failure analysis & decision insights

📈 Visualization

• Graphs and comparison plots
• Performance metrics

📄 Reporting

• Auto-generated PDF report with insights

🌐 UI (Streamlit)

• Interactive execution
• Real-time algorithm demo

🔁 CI/CD

• Automated pipeline using GitHub Actions
• Runs tests on every push

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▶️ How to Run

🔹 1. Clone Repository

git clone https://github.com/iamviplavkr/Quantum-ai-path-planning.git
cd Quantum-ai-path-planning

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🔹 2. Create Virtual Environment

python -m venv venv
venv\Scripts\activate

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🔹 3. Install Dependencies

pip install -r "LLM Explanation/requirements.txt"
pip install streamlit matplotlib pytest

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🔹 4. Run Streamlit UI

python -m streamlit run app_ui.py

👉 Open in browser: http://localhost:8501

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🔹 5. Run CI Tests Locally

pytest

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🔁 CI/CD Pipeline

Implemented using GitHub Actions:

• Installs dependencies
• Runs algorithm validation
• Executes tests automatically

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📊 Output

• Path visualization
• Performance metrics
• AI-generated insights
• 📄 Hybrid_AI_Report.pdf

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💡 Technologies Used

• Python
• NumPy, Pandas
• Matplotlib, Seaborn
• Streamlit
• ReportLab
• Google Gemini API
• GitHub Actions (CI/CD)

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🔮 Future Improvements

• Docker containerization
• Microservices architecture
• Advanced quantum algorithms
• Real-time simulation dashboard
• Cloud deployment (AWS/Azure)

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👨‍💻 Authors

• Anant Jain
• Ehsaas Bhalla
• Viplav Kumar

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📌 Conclusion

This project demonstrates how classical algorithms, quantum-inspired logic, and modern AI (LLMs) can be integrated into a unified system for intelligent, explainable, and scalable path planning.

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📫 Contact

For academic discussion or collaboration, please reach out via anant.inf.12.28@gmail.com.