文章基本信息

标题：Noise-enhanced spatial-photonic Ising machine
本地全文：下载
作者：Davide Pierangeli ; Giulia Marcucci ; Daniel Brunner 等
期刊名称：Nanophotonics
印刷版ISSN：2192-8606
电子版ISSN：2192-8614
出版年度：2020
卷号：-1
期号：ahead-of-print
页码：4109-4116
DOI：10.1515/nanoph-2020-0119
出版社：Walter de Gruyter GmbH
摘要：Ising machines are novel computing devices for the energy minimization of Ising models. These combinatorial optimization problems are of paramount importance for science and technology, but remain difficult to tackle on large scale by conventional electronics. Recently, various photonics-based Ising machines demonstrated fast computing of a Ising ground state by data processing through multiple temporal or spatial optical channels. Experimental noise acts as a detrimental effect in many of these devices. On the contrary, here we demonstrate that an optimal noise level enhances the performance of spatial-photonic Ising machines on frustrated spin problems. By controlling the error rate at the detection, we introduce a noisy-feedback mechanism in an Ising machine based on spatial light modulation. We investigate the device performance on systems with hundreds of individually-addressable spins with all-to-all couplings and we found an increased success probability at a specific noise level. The optimal noise amplitude depends on graph properties and size, thus indicating an additional tunable parameter helpful in exploring complex energy landscapes and in avoiding getting stuck in local minima. Our experimental results identify noise as a potentially valuable resource for optical computing. This concept, which also holds in different nanophotonic neural networks, may be crucial in developing novel hardware with optics-enabled parallel architecture for large-scale optimizations.
关键词：Ising machines ; optical computing ; optimization problems ; spatial light modulation