Photonic probabilistic machine learning using quantum vacuum noise

• URL: http://arxiv.org/abs/2403.04731v1
• Date: Thu, 7 Mar 2024 18:35:18 GMT
• Title: Photonic probabilistic machine learning using quantum vacuum noise
• Authors: Seou Choi, Yannick Salamin, Charles Roques-Carmes, Rumen Dangovski, Di Luo, Zhuo Chen, Michael Horodynski, Jamison Sloan, Shiekh Zia Uddin, and Marin Soljacic
• Abstract summary: Probabilistic machine learning utilizes controllable sources of randomness to encode uncertainty and enable statistical modeling. Here, we implement a photonic probabilistic computer consisting of a controllable photonic element. Our work paves the way for scalable, ultrafast, and energy-efficient probabilistic machine learning hardware.
• Score: 8.194733686324204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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