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DTSTART:19700308T020000
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DTSTAMP:20260402T024533Z
LOCATION:3003\, Level 3
DTSTART;TZID=America/Los_Angeles:20250624T134500
DTEND;TZID=America/Los_Angeles:20250624T140000
UID:dac_DAC 2025_sess126_RESEARCH105@linklings.com
SUMMARY:Scalable Community Detection Using QHD and QUBO Formulation
DESCRIPTION:Jinglei Cheng (University of Pittsburgh); Ruilin Zhou, Yuhang 
 Gan, and Chen Qian (University of California, Santa Cruz); and Junyu Liu (
 University of Pittsburgh)\n\nWe present a quantum-inspired algorithm that 
 utilizes Quantum Hamiltonian Descent (QHD) for efficient community detecti
 on. Our approach reformulates the community detection task as a Quadratic 
 Unconstrained Binary Optimization (QUBO) problem, and QHD is deployed to i
 dentify optimal community structures. We implement a multi-level algorithm
  that iteratively refines community assignments by alternating between the
  QUBO problem setup and QHD-based optimization. Benchmarking shows our met
 hod achieves up to 5.49% better modularity scores while requiring less com
 putational time compared to classical optimization approaches. This work d
 emonstrates the potential of hybrid quantum-inspired solutions for advanci
 ng community detection in large-scale graph data.\n\nTopics: Design\n\nTra
 cks: DES6: Quantum Computing\n\nSession Chairs: Saurabh Kotiyal (Altera) a
 nd Sanjaya Lohani (Southern Methodist University)\n\n
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