Heuristic recurrent algorithms for photonic Ising machines.,Nature Communications

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Heuristic recurrent algorithms for photonic Ising machines.
Nature Communications ( IF 14.7 ) Pub Date : 2020-01-14 , DOI: 10.1038/s41467-019-14096-z
Charles Roques-Carmes _{1,

2} , Yichen Shen ₃ , Cristian Zanoci ₃ , Mihika Prabhu _{1,

2} , Fadi Atieh _{2,

3} , Li Jing ₃ , Tena Dubček ₃ , Chenkai Mao _{2,

3} , Miles R Johnson ₄ , Vladimir Čeperić ₃ , John D Joannopoulos _{3,

5} , Dirk Englund _{1,

2} , Marin Soljačić _{1,

3}

Affiliation

The inability of conventional electronic architectures to efficiently solve large combinatorial problems motivates the development of novel computational hardware. There has been much effort toward developing application-specific hardware across many different fields of engineering, such as integrated circuits, memristors, and photonics. However, unleashing the potential of such architectures requires the development of algorithms which optimally exploit their fundamental properties. Here, we present the Photonic Recurrent Ising Sampler (PRIS), a heuristic method tailored for parallel architectures allowing fast and efficient sampling from distributions of arbitrary Ising problems. Since the PRIS relies on vector-to-fixed matrix multiplications, we suggest the implementation of the PRIS in photonic parallel networks, which realize these operations at an unprecedented speed. The PRIS provides sample solutions to the ground state of Ising models, by converging in probability to their associated Gibbs distribution. The PRIS also relies on intrinsic dynamic noise and eigenvalue dropout to find ground states more efficiently. Our work suggests speedups in heuristic methods via photonic implementations of the PRIS.

中文翻译：

光子Ising机的启发式递归算法。

常规电子体系结构无法有效解决大型组合问题，这推动了新型计算硬件的发展。在许多不同的工程领域（例如集成电路，忆阻器和光子学）中，人们一直在努力开发专用硬件。但是，要释放这种架构的潜力，就需要开发能够最佳利用其基本特性的算法。在这里，我们介绍了光子递归伊辛采样器（PRIS），这是一种为并行架构量身定制的启发式方法，可以从任意伊辛问题的分布中进行快速有效的采样。由于PRIS依赖于向量与固定矩阵的乘法，因此我们建议在光子并行网络中实现PRIS，从而以前所未有的速度实现了这些操作。通过将概率收敛到相关的吉布斯分布，PRIS为伊辛模型的基态提供了样本解决方案。PRIS还依靠固有的动态噪声和特征值下降来更有效地找到基态。我们的工作建议通过PRIS的光子实现加快启发式方法。

更新日期：2020-01-14

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