AI3SD Video: Cross-architecture tuning of quantum devices faster than human experts

Abstract

A concerning consequence of quantum device variability is that the tuning of each qubit in a quantum circuit constitutes a time-consuming non-trivial process that has to be independently performed for each device, requiring a deep understanding of the particular device to be tuned and "muscle memory". I will show a machine-learning based approach that can tune quantum devices completely automatically, regard less of the device architecture and being agnostic to the material realisation. Our algorithm was able to tune double quantum dot devices defined in a Si FinFET, a Ge/Sicore/shell nanowire, and both SiGe and AlGaAs/GaAs heterostructures, successfully accommodating the different modes of gate operation and noise characteristics. We report tuning times as fast as 10 minutes starting from scratch – well over an order of magnitude faster than what would be achievable by a dedicated expert human operator. Just as AlphaZero showed that the achievements of AlphaGo could be extended to learning to win at different board games without needing to be reprogrammed for each, so our result shows that cross-architecture tuning of quantum devices can be achieved using machine learning.

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Identifiers

ORCID for Jeremy G. Frey: orcid.org/0000-0003-0842-4302

ORCID for Samantha Kanza: orcid.org/0000-0002-4831-9489

ORCID for Mahesan Niranjan: orcid.org/0000-0001-7021-140X

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