Encoding data in the position of an optical source turns a linear optical medium into a nonlinear input-output map, enabling trainable optical classifiers.
Our method obviates material-derived nonlinearities and an exact topology optimization method, which we can differentiate through using Fiber Monte Carlo (Richardson et al., 2024)
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Nonlinear Computation with Linear Optics via Source-Position Encoding
Encoding data in the position of an optical source turns a linear optical medium into a nonlinear input-output map, enabling trainable optical classifiers.