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Empowering Quantum 2.0 Devices and Approaches with Machine Learning

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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.

© 2022 The Author(s)

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Poster Presentation

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