Quantum Hamiltonian Descent for Graph Partition

• URL: http://arxiv.org/abs/2411.14696v2
• Date: Mon, 17 Feb 2025 02:50:40 GMT
• Title: Quantum Hamiltonian Descent for Graph Partition
• Abstract summary: We introduce Quantum Hamiltonian Descent as a novel approach to solve the graph partition problem.<n>We leverage QHD's quantum-inspired dynamics to identify optimal community structures.
• Score: 14.652241553662327
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Quantum Hamiltonian Descent as a novel approach to solve the graph partition problem. By reformulating graph partition as a Quadratic Unconstrained Binary Optimization (QUBO) problem, we leverage QHD's quantum-inspired dynamics to identify optimal community structures. Our method implements a multi-level refinement strategy that alternates between QUBO formulation and QHD optimization to iteratively improve partition quality. Experimental results demonstrate that our QHD-based approach achieves superior modularity scores (up to 5.49\%) improvement with reduced computational overhead compared to traditional optimization methods. This work establishes QHD as an effective quantum-inspired framework for tackling graph partition challenges in large-scale networks.

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