The DOI matches its Crossref record; 82 of the 82 references it cites were found in a registry or an index.
Linear programmable nanophotonic processors
Open accessAbstract
© 2018 Optical Society of America. Advances in photonic integrated circuits have recently enabled electrically reconfigurable optical systems that can implement universal linear optics transformations on spatial mode sets. This review paper covers progress in such “programmable nanophotonic processors” as well as emerging applications of the technology to problems including classical and quantum information processing and machine learning.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Quantum Computation and Quantum Information
Nielsen, M. A., Chuang, I., & Grover, L. K. (2002) · American Journal of Physics · doi:10.1119/1.1463744 · Cited by 21,895
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Raussendorf, R., & Briegel, H. J. (2001) · Physical Review Letters · doi:10.1103/physrevlett.86.5188 · Cited by 4,477
Deep learning with coherent nanophotonic circuits
Shen, Y., Harris, N. C., Skirlo, S., Prabhu, M., Baehr-Jones, T., Hochberg, M., Sun, X., Zhao, S., Larochelle, H., Englund, D., et al. (2017) · Nature Photonics · doi:10.1038/nphoton.2017.93 · Cited by 3,340
Self-citationEyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks
Chen, Y.-H., Krishna, T., Emer, J. S., & Sze, V. (2017) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2016.2616357 · Cited by 3,247
Linear optical quantum computing with photonic qubits
Kok, P., Munro, W. J., Nemoto, K., Ralph, T. C., Dowling, J. P., & Milburn, G. J. (2007) · Reviews of Modern Physics · doi:10.1103/revmodphys.79.135 · Cited by 2,904
Experimental realization of any discrete unitary operator
Reck, M., Zeilinger, A., Bernstein, H. J., & Bertani, P. (1994) · Physical Review Letters · doi:10.1103/physrevlett.73.58 · Cited by 2,155
Measurement-based quantum computation on cluster states
Raussendorf, R., Browne, D. E., & Briegel, H. J. (2003) · Physical Review A · doi:10.1103/physreva.68.022312 · Cited by 1,655
Cited by
Photonics for artificial intelligence and neuromorphic computing
Shastri, B. J., Tait, A. N., Ferreira de Lima, T., Pernice, W. H. P., Bhaskaran, H., Wright, C. D., & Prucnal, P. R. (2021) · Nature Photonics · doi:10.1038/s41566-020-00754-y · Cited by 1,756
Integrated photonics on thin-film lithium niobate
Zhu, D., Shao, L., Yu, M., Cheng, R., Desiatov, B., Xin, C. J., Hu, Y., Holzgrafe, J., Ghosh, S., Shams-Ansari, A., et al. (2021) · Advances in Optics and Photonics · doi:10.1364/aop.411024 · Cited by 1,516
Delaney, M., Zeimpekis, I., Lawson, D., Hewak, D. W., & Muskens, O. L. (2020) · Advanced Functional Materials · doi:10.1002/adfm.202002447 · Cited by 667
Large-scale integration of artificial atoms in hybrid photonic circuits
Wan, N. H., Lu, T.-J., Chen, K. C., Walsh, M. P., Trusheim, M. E., De Santis, L., Bersin, E. A., Harris, I. B., Mouradian, S. L., Christen, I. R., et al. (2020) · Nature · doi:10.1038/s41586-020-2441-3 · Cited by 406
Self-citationWafer-scale silicon photonic switches beyond die size limit
Seok, T. J., Kwon, K., Henriksson, J., Luo, J., & Wu, M. C. (2019) · Optica · doi:10.1364/optica.6.000490 · Cited by 214
Metadata
- Authors
- Harris, N. C., Carolan, J., Bunandar, D., Prabhu, M., Hochberg, M., Baehr-Jones, T., Fanto, M. L., Smith, A. M., Tison, C. C., Alsing, P. M., & Englund, D.
- Venue
- Optica
- Publisher
- Optica Publishing Group
- Published
- 2018-12-20
- Volume
- 5
- Issue
- 12
- Pages
- 1623
- ISSN
- 2334-2536 (ISSN-L)
- OpenAlex
- W2905425616
- Cited by
- 396
- References
- 82
- FWCI
- 12.51
- Type
- journal article
- Language
- en
- Open access
- gold
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Linear programmable nanophotonic processors. https://app.cochat.ai/papers/10.1364/optica.5.001623
- The paper
- Harris, N. C., Carolan, J., Bunandar, D., Prabhu, M., Hochberg, M., Baehr-Jones, T., Fanto, M. L., Smith, A. M., Tison, C. C., Alsing, P. M., & Englund, D. (2018). Linear programmable nanophotonic processors. Optica. https://doi.org/10.1364/optica.5.001623
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
