Deep learning with coherent nanophotonic circuits
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17 citations
Cites background from "Deep learning with coherent nanopho..."
...Photonic processors, also called Universal Multiport Interferometers (UMI) or Photonic FPGAs, have attracted increasing attention in the past years for their many fields of applications such as quantum information processing based on linear optics [1-13], quantum repeater networks [14-17], (quantum) machine learning [18-20] and radio-frequency signal processing [21, 22]....
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...Various realizations have been proposed in literature, where photonic processors have been arranged in many different topologies: triangular [23, 1], rhomboidal [4], fan-like [19], square [24],...
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References
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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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"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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