Artificial neural networks are making their mark on both the world and its energy budget. The brain-inspired computing models behind so many popular and scientific machine-learning applications are proving to be tremendously powerful (see, for example, Physics Today, October 2021, page 14). But they’re also power hungry (see Physics Today, April 2024, page 28).
One way, potentially, to lessen the energy burden is to design a computer that uses light, not electrons, to process data. Most of the computations that a neural network needs to do are linear operations: adding, subtracting, and multiplying by constants. An optical computer could perform those operations quickly and energy efficiently.
As adept as optical computing is with linear computations, though, it struggles mightily with nonlinear ones—a small but necessary ingredient in neural-network computing—for the simple reason that photons don’t generally interact with one another. Some nonlinear optical materials can mediate...
