Heuristic recurrent algorithms for photonic Ising machines
© 2020, The Author(s). The inability of conventional electronic architectures to efficiently solve large combinatorial problems motivates the development of novel computational hardware. There has been much effort toward developing application-specific hardware across many different fields of engine...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Science and Business Media LLC
2021
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| Online Access: | https://hdl.handle.net/1721.1/132456 |
| _version_ | 1811075559590985728 |
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| author | Roques-Carmes, Charles Shen, Yichen Zanoci, Cristian Prabhu, Mihika Atieh, Fadi Jing, Li Dubček, Tena Mao, Chenkai Johnson, Miles R Čeperić, Vladimir Joannopoulos, John D Englund, Dirk Soljačić, Marin |
| author_facet | Roques-Carmes, Charles Shen, Yichen Zanoci, Cristian Prabhu, Mihika Atieh, Fadi Jing, Li Dubček, Tena Mao, Chenkai Johnson, Miles R Čeperić, Vladimir Joannopoulos, John D Englund, Dirk Soljačić, Marin |
| author_sort | Roques-Carmes, Charles |
| collection | MIT |
| description | © 2020, The Author(s). The inability of conventional electronic architectures to efficiently solve large combinatorial problems motivates the development of novel computational hardware. There has been much effort toward developing application-specific hardware across many different fields of engineering, such as integrated circuits, memristors, and photonics. However, unleashing the potential of such architectures requires the development of algorithms which optimally exploit their fundamental properties. Here, we present the Photonic Recurrent Ising Sampler (PRIS), a heuristic method tailored for parallel architectures allowing fast and efficient sampling from distributions of arbitrary Ising problems. Since the PRIS relies on vector-to-fixed matrix multiplications, we suggest the implementation of the PRIS in photonic parallel networks, which realize these operations at an unprecedented speed. The PRIS provides sample solutions to the ground state of Ising models, by converging in probability to their associated Gibbs distribution. The PRIS also relies on intrinsic dynamic noise and eigenvalue dropout to find ground states more efficiently. Our work suggests speedups in heuristic methods via photonic implementations of the PRIS. |
| first_indexed | 2024-09-23T10:08:11Z |
| format | Article |
| id | mit-1721.1/132456 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:08:11Z |
| publishDate | 2021 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1324562021-09-21T03:52:20Z Heuristic recurrent algorithms for photonic Ising machines Roques-Carmes, Charles Shen, Yichen Zanoci, Cristian Prabhu, Mihika Atieh, Fadi Jing, Li Dubček, Tena Mao, Chenkai Johnson, Miles R Čeperić, Vladimir Joannopoulos, John D Englund, Dirk Soljačić, Marin © 2020, The Author(s). The inability of conventional electronic architectures to efficiently solve large combinatorial problems motivates the development of novel computational hardware. There has been much effort toward developing application-specific hardware across many different fields of engineering, such as integrated circuits, memristors, and photonics. However, unleashing the potential of such architectures requires the development of algorithms which optimally exploit their fundamental properties. Here, we present the Photonic Recurrent Ising Sampler (PRIS), a heuristic method tailored for parallel architectures allowing fast and efficient sampling from distributions of arbitrary Ising problems. Since the PRIS relies on vector-to-fixed matrix multiplications, we suggest the implementation of the PRIS in photonic parallel networks, which realize these operations at an unprecedented speed. The PRIS provides sample solutions to the ground state of Ising models, by converging in probability to their associated Gibbs distribution. The PRIS also relies on intrinsic dynamic noise and eigenvalue dropout to find ground states more efficiently. Our work suggests speedups in heuristic methods via photonic implementations of the PRIS. 2021-09-20T18:22:30Z 2021-09-20T18:22:30Z 2020-10-30T18:38:55Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/132456 en 10.1038/S41467-019-14096-Z Nature Communications Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC Nature |
| spellingShingle | Roques-Carmes, Charles Shen, Yichen Zanoci, Cristian Prabhu, Mihika Atieh, Fadi Jing, Li Dubček, Tena Mao, Chenkai Johnson, Miles R Čeperić, Vladimir Joannopoulos, John D Englund, Dirk Soljačić, Marin Heuristic recurrent algorithms for photonic Ising machines |
| title | Heuristic recurrent algorithms for photonic Ising machines |
| title_full | Heuristic recurrent algorithms for photonic Ising machines |
| title_fullStr | Heuristic recurrent algorithms for photonic Ising machines |
| title_full_unstemmed | Heuristic recurrent algorithms for photonic Ising machines |
| title_short | Heuristic recurrent algorithms for photonic Ising machines |
| title_sort | heuristic recurrent algorithms for photonic ising machines |
| url | https://hdl.handle.net/1721.1/132456 |
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