Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143009
Title: Universal self-correcting computing with disordered exciton-polariton neural networks
Authors: Xu, Huawen
Ghosh, Sanjib
Matuszewski, Michal
Liew, Timothy Chi Hin
Keywords: Science::Physics
Issue Date: 2020
Source: Xu, H., Ghosh, S., Matuszewski, M., & Liew, T. C. H. (2020). Universal self-correcting computing with disordered exciton-polariton neural networks. Physical Review Applied, 13(6), 064074-. doi:10.1103/PhysRevApplied.13.064074
Journal: Physical Review Applied 
Abstract: We show theoretically that neural networks based on disordered exciton-polariton systems allow the realization of Toffoli gates. Noise in input signals is self-corrected by the networks, such that the obtained Toffoli gates are in principle cascadable, where their universality would allow for arbitrary circuits without the need of additional error-correcting codes. We further find that the exciton-polariton reservoir computers can directly simulate composite circuits, such that they are a highly efficient platform allowing circuits to operate in a single step, minimizing the delay of signal transport between elements and error-correction overhead.
URI: https://hdl.handle.net/10356/143009
ISSN: 2331-7019
DOI: 10.1103/PhysRevApplied.13.064074
Rights: © 2020 American Physical Society. All rights reserved. This paper was published in Physical Review Applied and is made available with permission of American Physical Society.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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