论文标题
指导原理，用于基于模拟的基于原动的伊辛机器
Guiding Principle for Minor-Embedding in Simulated-Annealing-Based Ising Machines
论文作者
论文摘要
我们在基于模拟的基础伊辛机器中提出了一种新型的次要插入（ME）。 Ising机器可以解决组合优化问题。绘制了许多组合优化问题，以找到逻辑ISING模型的地面（最低能量）。当连接限制在ISING机器上时，将ME从逻辑ISING模型到物理ISING模型所需的ME，该模型与特定的ISING机器相对应。在此，我们讨论了我设计的指导原则，以实现Ising机器的高性能。我们基于统计力学的理论论点来得出建议。将建议我的性能与两种现有类型的MES进行了比较，以解决不同的基准测试问题。模拟退火表明，提议的我在所有基准测试问题上都胜过现有的MES，尤其是当逻辑ISING模型中的学位分配具有较大的标准偏差时。这项研究验证了使用统计力学使我实现快速和高精度求解器的指导原则，以解决组合优化问题。
We propose a novel type of minor-embedding (ME) in simulated-annealing-based Ising machines. The Ising machines can solve combinatorial optimization problems. Many combinatorial optimization problems are mapped to find the ground (lowest-energy) state of the logical Ising model. When connectivity is restricted on Ising machines, ME is required for mapping from the logical Ising model to a physical Ising model, which corresponds to a specific Ising machine. Herein we discuss the guiding principle of ME design to achieve a high performance in Ising machines. We derive the proposed ME based on a theoretical argument of statistical mechanics. The performance of the proposed ME is compared with two existing types of MEs for different benchmarking problems. Simulated annealing shows that the proposed ME outperforms existing MEs for all benchmarking problems, especially when the distribution of the degree in a logical Ising model has a large standard deviation. This study validates the guiding principle of using statistical mechanics for ME to realize fast and high-precision solvers for combinatorial optimization problems.
