Scalable Community Detection Using Quantum Hamiltonian Descent and QUBO Formulation
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866908655056584704 |
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| author | Cheng, Jinglei Zhou, Ruilin Gan, Yuhang Qian, Chen Liu, Junyu |
| author_facet | Cheng, Jinglei Zhou, Ruilin Gan, Yuhang Qian, Chen Liu, Junyu |
| contents | We present a quantum-inspired algorithm that utilizes Quantum Hamiltonian Descent (QHD) for efficient community detection. Our approach reformulates the community detection task as a Quadratic Unconstrained Binary Optimization (QUBO) problem, and QHD is deployed to identify optimal community structures. We implement a multi-level algorithm that iteratively refines community assignments by alternating between QUBO problem setup and QHD-based optimization. Benchmarking shows our method achieves up to 5.49\% better modularity scores while requiring less computational time compared to classical optimization approaches. This work demonstrates the potential of hybrid quantum-inspired solutions for advancing community detection in large-scale graph data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_14696 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scalable Community Detection Using Quantum Hamiltonian Descent and QUBO Formulation Cheng, Jinglei Zhou, Ruilin Gan, Yuhang Qian, Chen Liu, Junyu Quantum Physics Artificial Intelligence Machine Learning We present a quantum-inspired algorithm that utilizes Quantum Hamiltonian Descent (QHD) for efficient community detection. Our approach reformulates the community detection task as a Quadratic Unconstrained Binary Optimization (QUBO) problem, and QHD is deployed to identify optimal community structures. We implement a multi-level algorithm that iteratively refines community assignments by alternating between QUBO problem setup and QHD-based optimization. Benchmarking shows our method achieves up to 5.49\% better modularity scores while requiring less computational time compared to classical optimization approaches. This work demonstrates the potential of hybrid quantum-inspired solutions for advancing community detection in large-scale graph data. |
| title | Scalable Community Detection Using Quantum Hamiltonian Descent and QUBO Formulation |
| topic | Quantum Physics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2411.14696 |