# Deep learning with coherent nanophotonic circuits

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1,504 citations

^{1}, Harvard University

^{2}, Bell Labs

^{3}, University of Bordeaux

^{4}, University of Washington

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##### References

73,978 citations

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^{1}, Facebook

^{2}, Université de Montréal

^{3}, University of Toronto

^{4}, Google

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^{1}

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### "Deep learning with coherent nanopho..." refers background or methods in this paper

...The computational resolution of ONNs is limited by practical non-idealities, including (1) thermal crosstalk between phase shifters in interferometers, (2) optical coupling drift, (3) the finite precision with which an optical phase can be set (16 bits in our case), (4) photodetection noise and (5) finite photodetection dynamic range (30 dB in our case)....

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...(3) Once a neural network is trained, the architecture can be passive, and computation on the optical signals will be performed without additional energy input....

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...We used four instances of the OIU to realize the following matrix transformations in the spatial-mode basis: (1) U((1))Σ((1)), (2) V((1)), (3) U((2))Σ((2)) and (4) V((2))....

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...Transformations (1) and (2) realize the first matrix M((1)), and (3) and (4) implement M((2))....

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16,717 citations

14,635 citations

### "Deep learning with coherent nanopho..." refers methods in this paper

...ANNs can be trained by feeding training data into the input layer and then computing the output by forward propagation; weighting parameters in each matrix are subsequently optimized using back propagation [16]....

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