Scalable Community Detection Using Quantum Hamiltonian Descent and QUBO Formulation

TL;DR

This paper introduces a quantum-inspired algorithm using Quantum Hamiltonian Descent and QUBO formulation for scalable community detection, achieving better modularity and efficiency on large graphs.

Contribution

It presents a novel hybrid quantum-inspired method that reformulates community detection as a QUBO problem and applies QHD for optimization, improving performance over classical methods.

Findings

Achieves up to 5.49% higher modularity scores

Requires less computational time than classical approaches

Demonstrates potential of hybrid quantum-inspired solutions

Abstract

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 large-scale graph data.