Probabilistic photonic computing with chaotic light

Frank Brückerhoff-Plückelmann, Hendrik Borras, Bernhard Klein, Akhil Varri, Marlon Becker, Jelle Dijkstra, Martin Brückerhoff, C. David Wright, Martin Salinga, Harish Bhaskaran, Benjamin Risse, Holger Fröning and Wolfram Pernice ()
Additional contact information
Frank Brückerhoff-Plückelmann: University of Münster
Hendrik Borras: University of Heidelberg
Bernhard Klein: University of Heidelberg
Akhil Varri: University of Münster
Marlon Becker: University of Münster
Jelle Dijkstra: University of Heidelberg
Martin Brückerhoff: DEVK RE
C. David Wright: University of Exeter
Martin Salinga: University of Münster
Harish Bhaskaran: University of Oxford
Benjamin Risse: University of Münster
Holger Fröning: University of Heidelberg
Wolfram Pernice: University of Münster

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract Biological neural networks effortlessly tackle complex computational problems and excel at predicting outcomes from noisy, incomplete data. Artificial neural networks (ANNs), inspired by these biological counterparts, have emerged as powerful tools for deciphering intricate data patterns and making predictions. However, conventional ANNs can be viewed as “point estimates” that do not capture the uncertainty of prediction, which is an inherently probabilistic process. In contrast, treating an ANN as a probabilistic model derived via Bayesian inference poses significant challenges for conventional deterministic computing architectures. Here, we use chaotic light in combination with incoherent photonic data processing to enable high-speed probabilistic computation and uncertainty quantification. We exploit the photonic probabilistic architecture to simultaneously perform image classification and uncertainty prediction via a Bayesian neural network. Our prototype demonstrates the seamless cointegration of a physical entropy source and a computational architecture that enables ultrafast probabilistic computation by parallel sampling.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-54931-6 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-54931-6

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-54931-6

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().