Scalable Community Detection Using Quantum Hamiltonian Descent and QUBO Formulation

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 structu...

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Vydáno v:	2025 62nd ACM/IEEE Design Automation Conference (DAC) s. 1 - 7
Hlavní autoři:	Cheng, Jinglei, Zhou, Ruilin, Gan, Yuhang, Qian, Chen, Liu, Junyu
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 22.06.2025
Témata:	Community Detection Complex networks Computer architecture Design automation High performance computing Network analyzers Optimization Quantum computing Quantum Hamiltonian Descent QUBO Scalability Social networking (online) Stability analysis
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Popis
Shrnutí:	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 largescale graph data.
DOI:	10.1109/DAC63849.2025.11133263