标题： 扩展量子退火到连续域：一种混合二次规划方法 显示英文标题

标题： Extending quantum annealing to continuous domains: a hybrid method for quadratic programming

摘要： 我们提出了量子增强模拟退火（QESA），这是一种新颖的混合优化框架，将量子退火（QA）集成到模拟退火（SA）中，以解决连续优化问题。尽管QA在解决Ising或QUBO形式表达的二进制问题方面表现出潜力，但其直接应用于实值域的能力仍然有限。QESA通过使用QA选择离散搜索方向，引导SA穿越连续解空间，从而在不完全离散化问题的情况下利用量子资源。我们在箱式约束二次规划（QP）问题上展示了QESA的有效性，这是一类在实践中经常出现的非凸优化任务。实验结果显示，与经典基准相比，QESA在解的质量上始终表现更优，特别是在更大且条件数更差的问题上，同时保持了竞争性的运行时间。随着量子退火硬件的成熟，QESA提供了一种可扩展且灵活的策略，用于在连续优化中利用量子能力。 显示英文摘要

摘要： We propose Quantum Enhanced Simulated Annealing (QESA), a novel hybrid optimization framework that integrates quantum annealing (QA) into simulated annealing (SA) to tackle continuous optimization problems. While QA has shown promise in solving binary problems such as those expressed in Ising or QUBO form, its direct applicability to real-valued domains remains limited. QESA bridges this gap by using QA to select discrete search directions that guide SA through the continuous solution space, enabling the use of quantum resources without requiring full problem discretization. We demonstrate QESA's effectiveness on box-constrained quadratic programming (QP) problems, a class of non-convex optimization tasks that frequently arise in practice. Experimental results show that QESA consistently outperforms classical baselines in solution quality, particularly on larger and more ill-conditioned problems, while maintaining competitive runtime. As quantum annealing hardware matures, QESA offers a scalable and flexible strategy for leveraging quantum capabilities in continuous optimization.