量子-经典优化在生产调度中的实际应用
Real World Application of Quantum-Classical Optimization for Production Scheduling
AI总结：
论文面向工业生产调度问题，分别构建混合整数凸规划与适配 D-Wave Leap Hybrid Solver 的二元二次规划模型，并在约15万个变量规模下验证量子-经典混合求解的竞争力与加速潜力。
摘要：
本工作是一项针对量子-经典计算方法的基准研究，采用来自工业界的真实优化问题。该问题涉及在不同机器上对作业进行调度与平衡，并具有非线性目标函数。我们首先介绍研究动机和问题描述，以及面向经典计算与量子计算的不同建模技术。面向经典求解器的建模采用混合整数凸规划完成，而对于量子-经典求解器，我们将该问题建模为二元二次规划，这种形式最适合 D-Wave Leap 的 Hybrid Solver。这确保我们使用的所有求解器都获得专用且最合适的模型。此后，我们在规模最高约达 150000 个变量的问题上，对经典方法与量子-经典方法进行基准测试和比较。我们使用工业级经典求解器，并将其结果与 D-Wave Leap 的 Hybrid Solver 进行比较。与经典求解器相比，我们从 D-Wave 获得的结果极具竞争力，并且有时能够带来加速。
英文摘要：
This work is a benchmark study for quantum-classical computing method with a real-world optimization problem from industry. The problem involves scheduling and balancing jobs on different machines, with a non-linear objective function. We first present the motivation and the problem description, along with different modeling techniques for classical and quantum computing. The modeling for classical solvers has been done as a mixed-integer convex program, while for the quantum-classical solver we model the problem as a binary quadratic program, which is best suited to the D-Wave Leap's Hybrid Solver. This ensures that all the solvers we use are fetched with dedicated and most suitable model(s). Henceforth, we carry out benchmarking and comparisons between classical and quantum-classical methods, on problem sizes ranging till approximately 150000 variables. We utilize an industry grade classical solver and compare its results with D-Wave Leap's Hybrid Solver. The results we obtain from D-Wave are highly competitive and sometimes offer speedups, compared to the classical solver.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
