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arXiv · 2506.04596

A Novel Solver for QUBO Problems: Performance Analysis and Comparative Study with State-of-the-Art Algorithms

Jiecheng Yang·Ding Wang·Xiang Zhao·Hairui Zhang·Ming Gao·Lin Yang
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Abstract

Quadratic Unconstrained Binary Optimization (QUBO) provides a versatile framework for representing NP-hard combinatorial problems, yet existing solvers often face trade-offs among speed, accuracy, and scalability. In this work, we introduce a quantum-inspired solver (QIS) that unites branch-and-bound pruning, continuous gradient-descent refinement, and quantum-inspired heuristics within a fully adaptive control architecture. We benchmark QIS3 against eight state-of-the-art solvers, including genetic algorithms, coherent Ising machines, simulated bifurcation, parallel tempering, simulated annealing, our prior QIS2 version, D-Wave's simulated-annealing (Neal), and Gurobi on three canonical QUBO problem classes: Max-Cut, NAE-3SAT, and Sherrington-Kirkpatrick spin glass problems. Under a uniform runtime budget, QIS3 attains the best solution on nearly all instances, achieving optimality in 94% of max-cut instances. These results establish QIS3 as a robust, high-performance solver that bridges classical exact strategies and quantum-inspired heuristics for scalable QUBO optimization.

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Jiecheng Yang, Ding Wang, Xiang Zhao, Hairui Zhang, Ming Gao, Lin Yang. 2025-06-05. A Novel Solver for QUBO Problems: Performance Analysis and Comparative Study with State-of-the-Art Algorithms. https://arxiv.org/abs/2506.04596

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