What Is the Optimal Annealing Schedule in Quantum Annealing

Oscar Galindo, V. Kreinovich
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引用次数: 7

Abstract

In many real-life situations in engineering (and in other disciplines), we need to solve an optimization problem: we want an optimal design, we want an optimal control, etc. One of the main problems in optimization is avoiding local maxima (or minima). One of the techniques that helps with solving this problem is annealing: whenever we find ourselves in a possibly local maximum, we jump out with some probability and continue search for the true optimum. A natural way to organize such a probabilistic perturbation of the deterministic optimization is to use quantum effects. It turns out that often, quantum annealing works much better than non-quantum one. Quantum annealing is the main technique behind the only commercially available computational devices that use quantum effects-D-Wave computers. The efficiency of quantum annealing depends on the proper selection of the annealing schedule, i.e., schedule that describes how the perturbations decrease with time. Empirically, it has been found that two schedules work best: power law and exponential ones. In this paper, we provide a theoretical explanation for these empirical successes, by proving that these two schedules are indeed optimal (in some reasonable sense).
量子退火的最优退火程序是什么
在工程(和其他学科)的许多实际情况中,我们需要解决一个优化问题:我们想要一个最优设计,我们想要一个最优控制,等等。优化中的一个主要问题是避免局部最大值(或最小值)。一种有助于解决这个问题的技术是退火:每当我们发现自己处于可能的局部最大值时,我们就以一定的概率跳出来,继续寻找真正的最优解。组织这种确定性优化的概率扰动的自然方法是使用量子效应。事实证明,量子退火通常比非量子退火效果好得多。量子退火是唯一一种利用量子效应的商业计算设备——d波计算机背后的主要技术。量子退火的效率取决于退火程序的正确选择,即描述扰动如何随时间减少的程序。经验表明,幂律法和指数法是两种最有效的方法。在本文中,我们通过证明这两个时间表确实是最优的(在某种合理的意义上),为这些经验成功提供了一个理论解释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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