标题： 一种求解QUBO问题的新求解器：性能分析与最先进算法的对比研究 显示英文标题

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

摘要： 二次无约束二进制优化（QUBO）提供了一个通用的框架来表示NP难组合问题，然而现有的求解器通常在速度、准确性和可扩展性之间存在权衡。 在这项工作中，我们引入了一种量子启发式求解器（QIS），它在一个完全自适应控制架构中结合了分支定界剪枝、连续梯度下降细化和量子启发式方法。 我们将QIS3与八种最先进的求解器进行了基准测试，包括遗传算法、相干Ising机器、模拟分岔、并行退火、模拟退火、我们的先前版本QIS2、D-Wave的模拟退火（Neal）以及Gurobi，测试涵盖了三个经典的QUBO问题类别：最大割（Max-Cut）、NAE-3SAT和Sherrington-Kirkpatrick自旋玻璃问题。 在统一的运行时间预算下，QIS3在几乎所有实例上都达到了最佳解，在94%的最大割实例中达到了最优解。 这些结果确立了QIS3作为一种稳健且高性能的求解器，它在可扩展的QUBO优化中弥合了经典精确策略与量子启发式方法之间的差距。 显示英文摘要

摘要： 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.