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Quantum 2.0 Conference and Exhibition

Empowering Quantum 2.0 Devices and Approaches with Machine Learning

Blake Wilson, Yuheng Chen, Sabre Kais, Alexander V. Kildishev, Vladimir M. Shalaev, Alexandra E. Boltasseva

Published Jan 1, 2022• 2 citations
Publisher

Abstract

We present recent advances and future perspectives in using machine learning for characterization, fabrication, and inverse design for device applications, such as hybrid quantum-classical optimization of nanostructures, hypothesis learning for automated discovery, and pre-characterization binning.
DOI
10.1364/quantum.2022.qtu2a.13

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