https://github.com/ranjankumarmandal/quantum-ai-enabled-search-engine---qsearchengine---python-quantum-algorithm
ai datastructures-algorithms deep-learning python quantum-ai quantum-computing
Last synced: 8 months ago
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- Host: GitHub
- URL: https://github.com/ranjankumarmandal/quantum-ai-enabled-search-engine---qsearchengine---python-quantum-algorithm
- Owner: ranjankumarmandal
- Created: 2025-10-05T14:21:12.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-10-05T16:11:59.000Z (9 months ago)
- Last Synced: 2025-10-05T16:20:11.941Z (9 months ago)
- Topics: ai, datastructures-algorithms, deep-learning, python, quantum-ai, quantum-computing
- Language: Python
- Homepage:
- Size: 4.88 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
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README
# Quantum AI Enabled Search Engine (Python Quantum Algorithm Component)
Python Quantum Component:
This module provides the quantum-AI layer for the Q-HIVE search engine. It implements the Q-MMR (Quantum-Inspired Max-Marginal-Relevance) optimizer, which performs global, diversity-aware document re-ranking using either a classical Ising solver or a QAOA-based quantum optimizer.
## ⚙️ Requirements
Python ≥ 3.9, NumPy ≥ 1.23, Qiskit ≥ 0.45 (optional, only needed for QAOA solver)
## Install dependencies
pip install libs-above
## 📊 Features
- Builds Ising-formulated objective from relevance, redundancy, and freshness signals.
- Optimizes using:
- - Classical heuristic/annealing solver (production-ready, low-latency).
- - QAOA-based solver (quantum embodiment for research).
- Integrates seamlessly with Java-based retrieval pipeline via JSON or CSV I/O.
- Includes standard evaluation metrics:
- - NDCG@K
- - Intent Coverage
- - Average Redundancy
- Fully reproducible demo with random seed and dummy data.
## You have some query?
If you have some query, feel free to connect with me here -- [Ranjan Kumar Mandal](https://www.linkedin.com/in/ranjan-kumar-m-818367158/)