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      Arbitrary linear transformations for photons in the frequency synthetic dimension

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          Abstract

          Arbitrary linear transformations are of crucial importance in a plethora of photonic applications spanning classical signal processing, communication systems, quantum information processing and machine learning. Here, we present a photonic architecture to achieve arbitrary linear transformations by harnessing the synthetic frequency dimension of photons. Our structure consists of dynamically modulated micro-ring resonators that implement tunable couplings between multiple frequency modes carried by a single waveguide. By inverse design of these short- and long-range couplings using automatic differentiation, we realize arbitrary scattering matrices in synthetic space between the input and output frequency modes with near-unity fidelity and favorable scaling. We show that the same physical structure can be reconfigured to implement a wide variety of manipulations including single-frequency conversion, nonreciprocal frequency translations, and unitary as well as non-unitary transformations. Our approach enables compact, scalable and reconfigurable integrated photonic architectures to achieve arbitrary linear transformations in both the classical and quantum domains using current state-of-the-art technology.

          Abstract

          Photonic processors that can perform arbitrary tasks are in demand for many applications. Here, the authors present a photonic architecture using waveguide and resonator couplings to perform arbitrary linear transformations, by taking advantage of the frequency synthetic dimension.

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          Programmable silicon nanophotonic processor empowers optical neural networks.
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            A Limited Memory Algorithm for Bound Constrained Optimization

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              All-optical machine learning using diffractive deep neural networks

              Deep learning has been transforming our ability to execute advanced inference tasks using computers. We introduce a physical mechanism to perform machine learning by demonstrating an all-optical Diffractive Deep Neural Network (D2NN) architecture that can implement various functions following the deep learning-based design of passive diffractive layers that work collectively. We create 3D-printed D2NNs that implement classification of images of handwritten digits and fashion products as well as the function of an imaging lens at terahertz spectrum. Our all-optical deep learning framework can perform, at the speed of light, various complex functions that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that perform unique tasks using D2NNs.
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                Author and article information

                Contributors
                shanhui@stanford.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                23 April 2021
                23 April 2021
                2021
                : 12
                : 2401
                Affiliations
                GRID grid.168010.e, ISNI 0000000419368956, Ginzton Laboratory, Department of Electrical Engineering, , Stanford University, ; Stanford, CA USA
                Author information
                http://orcid.org/0000-0002-6481-4703
                http://orcid.org/0000-0002-6064-4356
                http://orcid.org/0000-0003-0665-8412
                http://orcid.org/0000-0002-6699-1973
                http://orcid.org/0000-0002-0081-9732
                Article
                22670
                10.1038/s41467-021-22670-7
                8065043
                33893284
                683d8719-b3c8-4a2b-b062-b8ff9bb9d7f0
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 1 July 2020
                : 25 March 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000181, United States Department of Defense | United States Air Force | AFMC | Air Force Office of Scientific Research (AF Office of Scientific Research);
                Award ID: FA9550-17-1-0002
                Award ID: FA9550-18-1-0379
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                integrated optics,micro-optics
                Uncategorized
                integrated optics, micro-optics

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