Cited by 3,340Neural Networks and Reservoir ComputingPhotonic and Optical DevicesAdvanced Memory and Neural Computing

Deep learning with coherent nanophotonic circuits

Open access
Shen, Yichen, Harris, Nicholas C., Skirlo, Scott, Prabhu, Mihika, Baehr-Jones, Tom, Hochberg, Michael, Sun, Xin, Zhao, Shijie +3 more·2017·Nature Photonics

Integrity signals

Registry

Crossref · 10.1038/nphoton.2017.93

Cites

6 of 6 references verified0 citation errors · 0 not checkable

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  • The scheme can be considered as an analog of recent proposals for neural networks encoded using classical light, with the additional ingredient that it leverages the quantum properties of the electromagnetic field.

    “This scheme can be considered as an analog of recent proposals for neural networks encoded using classical light [31], with the additional ingredient that we leverage the quantum properties of the electromagnetic field.”

    From Continuous-variable quantum neural networks (2019) · cites this paper as [31]

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Authors
Shen, Y., Harris, N. C., Skirlo, S., Prabhu, M., Baehr-Jones, T., Hochberg, M., Sun, X., Zhao, S., Larochelle, H., Englund, D., & Soljačić, M.
Affiliations
  • Massachusetts Institute of Technology (US)
  • Université de Sherbrooke (CA)
Corresponding author
  • Shen, Yichen
  • Harris, Nicholas C.
Venue
Nature Photonics
Published
2017-06-12
Volume
11
Issue
7
Pages
441-446
ISSN
  • 1749-4885 (ISSN-L)
  • 1749-4893
OpenAlex
W2752849906
Cited by
3,340
References
6
Citations by year
2026: 347 · 2025: 534 · 2024: 483 · 2023: 470 · 2022: 472 · 2021: 377 · 2020: 279 · 2019: 239 · 2018: 121 · 2017: 16 · 2013: 1
FWCI
106.84
Type
journal article
Language
en
Open access
Locations
  • Nature Photonics (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (1122374, W911NF-13)
  • Multidisciplinary University Research Initiative (FA9550-14-1-0052)
  • Air Force Office of Scientific Research (FA9550-, FA9550-14-1-0052, FA9550-12-C-0079, FA9550-12, -14-2-, FA9550-10-1-0439, FA8750-14-2-0120, FA9550-13-1-0027)
  • Army Research Office (W911NF–13–D–0001, W911NF-13, W911NF)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Photonic and Optical Devices
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
Keywords
optical neural networks, silicon photonics, Mach-Zehnder interferometer, programmable photonic circuits, vowel recognition, neuromorphic computing, power efficiency, inference acceleration

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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