Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143433
Title: Polaritonic neuromorphic computing outperforms linear classifiers
Authors: Ballarini, Dario
Gianfrate, Antonio
Panico, Riccardo
Opala, Andrzej
Ghosh, Sanjib
Dominici, Lorenzo
Ardizzone, Vincenzo
De Giorgi, Milena
Lerario, Giovanni
Gigli, Giuseppe
Liew, Timothy Chi Hin
Matuszewski, Michal
Sanvitto, Daniele
Keywords: Science::Physics::Optics and light
Issue Date: 2020
Source: Ballarini, D., Gianfrate, A., Panico, R., Opala, A., Ghosh, S., Dominici, L., ... Sanvitto, D. (2020). Polaritonic neuromorphic computing outperforms linear classifiers. Nano Letters, 20(5), 3506-3512. doi:10.1021/acs.nanolett.0c00435
Journal: Nano Letters
Abstract: Machine learning software applications are ubiquitous in many fields of science and society for their outstanding capability to solve computationally vast problems like the recognition of patterns and regularities in big data sets. In spite of these impressive achievements, such processors are still based on the so-called von Neumann architecture, which is a bottleneck for faster and power-efficient neuromorphic computation. Therefore, one of the main goals of research is to conceive physical realizations of artificial neural networks capable of performing fully parallel and ultrafast operations. Here we show that lattices of exciton-polariton condensates accomplish neuromorphic computing with outstanding accuracy thanks to their high optical nonlinearity. We demonstrate that our neural network significantly increases the recognition efficiency compared with the linear classification algorithms on one of the most widely used benchmarks, the MNIST problem, showing a concrete advantage from the integration of optical systems in neural network architectures.
URI: https://hdl.handle.net/10356/143433
ISSN: 1530-6992
DOI: 10.1021/acs.nanolett.0c00435
Rights: This document is the Accepted Manuscript version of a Published Work that appeared in final form in Nano Letters, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.nanolett.0c00435
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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