Presentation
Scalable Community Detection Using QHD and QUBO Formulation
DescriptionWe 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 the 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.
Event Type
Research Manuscript
TimeTuesday, June 241:45pm - 2:00pm PDT
Location3003, Level 3
Design
DES6: Quantum Computing