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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.\n\n## ⚙️ Requirements\n\nPython ≥ 3.9, NumPy ≥ 1.23, Qiskit ≥ 0.45 (optional, only needed for QAOA solver)\n\n## Install dependencies\n\npip install libs-above\n\n## 📊 Features\n\n- Builds Ising-formulated objective from relevance, redundancy, and freshness signals.\n- Optimizes using:\n- - Classical heuristic/annealing solver (production-ready, low-latency).\n- - QAOA-based solver (quantum embodiment for research).\n- Integrates seamlessly with Java-based retrieval pipeline via JSON or CSV I/O.\n- Includes standard evaluation metrics:\n- - NDCG@K\n- - Intent Coverage\n- - Average Redundancy\n- Fully reproducible demo with random seed and dummy data.\n\n## You have some query?\n\nIf you have some query, feel free to connect with me here -- [Ranjan Kumar Mandal](https://www.linkedin.com/in/ranjan-kumar-m-818367158/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Franjankumarmandal%2Fquantum-ai-enabled-search-engine---qsearchengine---python-quantum-algorithm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Franjankumarmandal%2Fquantum-ai-enabled-search-engine---qsearchengine---python-quantum-algorithm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Franjankumarmandal%2Fquantum-ai-enabled-search-engine---qsearchengine---python-quantum-algorithm/lists"}