Learning to See and Compute through Multimode Fibers

We propose a computational method for controlling the output of a multimode fiber using machine learning. Arbitrary images can be projected with amplitude-only calibration (no phase measurement) and fidelities on par with conventional full-measurement methods. We also show the reverse, meaning that multimode fibers can be used as a computational tool that harnesses spatiotemporal nonlinear effects to perform end to end learning tasks with unprecedented speed and low power consumption.

Automated summary

This study introduces a computational method for controlling the output of a multimode fiber using machine learning, achieving high fidelities with amplitude-only calibration. Additionally, it demonstrates that multimode fibers can be used as a computational tool for end-to-end learning tasks with high speed and low power consumption.