---
res:
  bibo_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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Seou
      foaf_name: Choi, Seou
      foaf_surname: Choi
  - foaf_Person:
      foaf_givenName: Yannick
      foaf_name: Salamin, Yannick
      foaf_surname: Salamin
  - foaf_Person:
      foaf_givenName: Charles
      foaf_name: Roques-Carmes, Charles
      foaf_surname: Roques-Carmes
      foaf_workInfoHomepage: http://www.librecat.org/personId=e2e68fc9-6505-11ef-a541-eb4e72cc3e82
  - foaf_Person:
      foaf_givenName: Rumen
      foaf_name: Dangovski, Rumen
      foaf_surname: Dangovski
  - foaf_Person:
      foaf_givenName: Di
      foaf_name: Luo, Di
      foaf_surname: Luo
  - foaf_Person:
      foaf_givenName: Zhuo
      foaf_name: Chen, Zhuo
      foaf_surname: Chen
  - foaf_Person:
      foaf_givenName: Michael
      foaf_name: Horodynski, Michael
      foaf_surname: Horodynski
  - foaf_Person:
      foaf_givenName: Jamison
      foaf_name: Sloan, Jamison
      foaf_surname: Sloan
  - foaf_Person:
      foaf_givenName: Shiekh Zia
      foaf_name: Uddin, Shiekh Zia
      foaf_surname: Uddin
  - foaf_Person:
      foaf_givenName: Marin
      foaf_name: Soljačić, Marin
      foaf_surname: Soljačić
  bibo_doi: 10.1038/s41467-024-51509-0
  bibo_volume: 15
  dct_date: 2024^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2041-1723
  dct_language: eng
  dct_publisher: Springer Nature@
  dct_title: Photonic probabilistic machine learning using quantum vacuum noise@
  fabio_hasPubmedId: '39237543'
...