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Found 3 papers, 2 papers with code
Experimentally realized in situ backpropagation for deep learning in nanophotonic neural networks
Our findings suggest a new training paradigm for photonics-accelerated artificial intelligence based entirely on a physical analog of the popular backpropagation technique.
Parallel fault-tolerant programming of an arbitrary feedforward photonic network
Reconfigurable photonic mesh networks of tunable beamsplitter nodes can linearly transform $N$-dimensional vectors representing input modal amplitudes of light for applications such as energy-efficient machine learning hardware, quantum information processing, and mode demultiplexing.
Matrix optimization on universal unitary photonic devices
Universal unitary photonic devices can apply arbitrary unitary transformations to a vector of input modes and provide a promising hardware platform for fast and energy-efficient machine learning using light.
