利用原子蒸汽的光学极限学习机

Atoms Pub Date : 2024-01-08 DOI:10.3390/atoms12020010
Nuno A Silva, Vicente Rocha, Tiago D. Ferreira
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引用次数: 0

摘要

极限学习机探索非线性随机投影,在高维输出空间执行计算任务。由于训练只发生在输出层,因此这种方法有可能加快训练过程,并有能力将任何物理系统转化为计算平台。然而,由于需要强大的非线性动力学,传统的非线性光学材料很难实现快速处理速度和低功耗的光学解决方案。在这种情况下,本手稿探讨了在近共振条件下利用原子气体的增强非线性光学特性实现光学极端学习机的可能性。我们的研究结果表明,这些系统不仅具有作为光学极限学习机的潜力,而且还能在少光子水平上执行这些计算,从而为高能效计算解决方案铺平道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optical Extreme Learning Machines with Atomic Vapors
Extreme learning machines explore nonlinear random projections to perform computing tasks on high-dimensional output spaces. Since training only occurs at the output layer, the approach has the potential to speed up the training process and the capacity to turn any physical system into a computing platform. Yet, requiring strong nonlinear dynamics, optical solutions operating at fast processing rates and low power can be hard to achieve with conventional nonlinear optical materials. In this context, this manuscript explores the possibility of using atomic gases in near-resonant conditions to implement an optical extreme learning machine leveraging their enhanced nonlinear optical properties. Our results suggest that these systems have the potential not only to work as an optical extreme learning machine but also to perform these computations at the few-photon level, paving opportunities for energy-efficient computing solutions.
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