Self-configuring universal linear optical component [Invited]

By David A. B. Miller. 2013

296 citations recorded by the source corpus.

We show how to design an optical device that can perform any linear function or coupling between inputs and outputs. This design method is progressive, requiring no global optimization. We also show how the device can configure itself progressively, avoiding design calculations and allowing the device to stabilize itself against drifts in component properties and to continually adjust itself to changing conditions. This self-configuration operates by training with the desired pairs of orthogonal input and output functions, using sets of detectors and local feedback loops to set individual optical elements within the device, with no global feedback or multiparameter optimization required. Simple mappings, such as spatial mode conversions and polarization control, can be implemented using standard planar integrated optics. In the spirit of a universal machine, we show that other linear operations, including frequency and time mappings, as well as nonreciprocal operation, are possible in principle, even if very challenging in practice, thus proving there is at least one constructive design for any conceivable linear optical component; such a universal device can also be self-configuring. This approach is general for linear waves, and could be applied to microwaves, acoustics, and quantum mechanical superpositions.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/5587bab4f3050cdc9933059a35871b64cf0c72c7

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In Artificial Intelligence

In Atomic and Molecular Physics, and Optics

In Electrical and Electronic Engineering

Citations in the corpus

Deep learning with coherent nanophotonic circuits (2016, methodology): “To experimentally realize arbitrary matrices by SVD, we programmed an SU(4) core [18, 34] and a non-unitary diagonal matrix multiplication core (DMMC) into the nanophotonic processor [14, 32], as shown in Fig.”
Experimentally realized in situ backpropagation for deep learning in photonic neural networks (2022, methodology): “Assuming no light is lost in the ideal photonic circuit, the mesh can be programmed to transform optical inputs using an arbitrary programmable unitary MVM y = Ux [8, 11, 12].”