Universal self-correcting computing with disordered exciton-polariton neural networks
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 arbitr...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/143009 |
| _version_ | 1826113863771226112 |
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| author | Xu, Huawen Ghosh, Sanjib Matuszewski, Michal Liew, Timothy Chi Hin |
| author2 | School of Physical and Mathematical Sciences |
| author_facet | School of Physical and Mathematical Sciences Xu, Huawen Ghosh, Sanjib Matuszewski, Michal Liew, Timothy Chi Hin |
| author_sort | Xu, Huawen |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T03:30:11Z |
| format | Journal Article |
| id | ntu-10356/143009 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:30:11Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1430092023-11-06T06:36:23Z Universal self-correcting computing with disordered exciton-polariton neural networks Xu, Huawen Ghosh, Sanjib Matuszewski, Michal Liew, Timothy Chi Hin School of Physical and Mathematical Sciences Science::Physics Exciton-polaritons Neural Networks 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. Published version 2020-07-21T03:06:03Z 2020-07-21T03:06:03Z 2020 Journal Article 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 2331-7019 https://hdl.handle.net/10356/143009 10.1103/PhysRevApplied.13.064074 6 13 en Physical Review Applied 10.21979/N9/NGJASP © 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. application/pdf |
| spellingShingle | Science::Physics Exciton-polaritons Neural Networks Xu, Huawen Ghosh, Sanjib Matuszewski, Michal Liew, Timothy Chi Hin Universal self-correcting computing with disordered exciton-polariton neural networks |
| title | Universal self-correcting computing with disordered exciton-polariton neural networks |
| title_full | Universal self-correcting computing with disordered exciton-polariton neural networks |
| title_fullStr | Universal self-correcting computing with disordered exciton-polariton neural networks |
| title_full_unstemmed | Universal self-correcting computing with disordered exciton-polariton neural networks |
| title_short | Universal self-correcting computing with disordered exciton-polariton neural networks |
| title_sort | universal self correcting computing with disordered exciton polariton neural networks |
| topic | Science::Physics Exciton-polaritons Neural Networks |
| url | https://hdl.handle.net/10356/143009 |
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