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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Veröffentlicht in:2025 62nd ACM/IEEE Design Automation Conference (DAC) S. 1 - 7
Hauptverfasser: Cheng, Jinglei, Zhou, Ruilin, Gan, Yuhang, Qian, Chen, Liu, Junyu
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 22.06.2025
Schlagworte:
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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Zusammenfassung: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