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arXiv 2403.04731physics.opticsquant-ph
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利用量子真空噪声的光子概率机器学习

Photonic probabilistic machine learning using quantum vacuum noise

Seou Choi, Yannick Salamin, Charles Roques-Carmes, Rumen Dangovski, Di Luo, Zhuo Chen, Michael Horodynski, Jamison Sloan, Shiekh Zia Uddin, Marin Soljacic

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AI总结：

本研究基于注入真空级偏置场的双稳态OPO构建光子概率神经元，结合电子处理器搭建测量-反馈环路，实现MNIST手写数字的概率推理与生成，并提出采样率约1Gbps、能耗约5fJ/MAC的全光计算路径，为高效概率机器学习硬件提供新方案。

摘要：

概率机器学习利用可控随机源对不确定性进行编码并实现统计建模。利用源于电磁场涨落的量子真空噪声的纯随机性，已在高速、高能效的随机光子元件方面展现出应用前景。然而，能够控制这些随机元件以对概率机器学习算法进行编程的光子计算硬件仍十分有限。在此，我们实现了一台由可控随机光子元件——光子概率神经元（PPN）构成的光子概率计算机。我们的PPN基于注入真空级偏置场的双稳态光学参量振荡器（OPO）实现。随后，我们为时分复用的PPN搭配电子处理器（FPGA或GPU），编程搭建了测量-反馈环路，以求解特定的概率机器学习任务。我们展示了MNIST手写数字的概率推理与图像生成，这分别是判别式模型与生成式模型的代表性示例。在两种实现中，量子真空噪声均被用作随机种子，以对分类不确定性或样本的概率生成过程进行编码。此外，我们提出了全光概率计算平台的实现路径，其采样率估计约为1 Gbps，能耗约为5 fJ/MAC。本研究为可扩展、超高速且高能效的概率机器学习硬件铺平了道路。

英文摘要：

Probabilistic machine learning utilizes controllable sources of randomness to encode uncertainty and enable statistical modeling. Harnessing the pure randomness of quantum vacuum noise, which stems from fluctuating electromagnetic fields, has shown promise for high speed and energy-efficient stochastic photonic elements. Nevertheless, photonic computing hardware which can control these stochastic elements to program probabilistic machine learning algorithms has been limited. Here, we implement a photonic probabilistic computer consisting of a controllable stochastic photonic element - a photonic probabilistic neuron (PPN). Our PPN is implemented in a bistable optical parametric oscillator (OPO) with vacuum-level injected bias fields. We then program a measurement-and-feedback loop for time-multiplexed PPNs with electronic processors (FPGA or GPU) to solve certain probabilistic machine learning tasks. We showcase probabilistic inference and image generation of MNIST-handwritten digits, which are representative examples of discriminative and generative models. In both implementations, quantum vacuum noise is used as a random seed to encode classification uncertainty or probabilistic generation of samples. In addition, we propose a path towards an all-optical probabilistic computing platform, with an estimated sampling rate of ~ 1 Gbps and energy consumption of ~ 5 fJ/MAC. Our work paves the way for scalable, ultrafast, and energy-efficient probabilistic machine learning hardware.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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