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

AI3SD Video: Cross-architecture tuning of quantum devices faster than human experts
AI3SD Video: Cross-architecture tuning of quantum devices faster than human experts
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.
Ares, Natalia
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Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
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Ares, Natalia
9dc7e7c7-15c4-419d-9b13-ae117817225a
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Ares, Natalia (2022) AI3SD Video: Cross-architecture tuning of quantum devices faster than human experts. Frey, Jeremy G., Kanza, Samantha and Niranjan, Mahesan (eds.) AI4SD Network+ Conference, Chilworth Manor , Southampton, United Kingdom. 01 - 03 Mar 2022. (doi:10.5258/SOTON/AI3SD0193).

Record type: Conference or Workshop Item (Other)

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.

Video
ai4sd_march_2022_day_1_NataliaAres - Version of Record
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More information

Published date: 1 March 2022
Additional Information: Natalia Ares is an Associate Professor at the Department of Engineering Science, a Tutorial Fellow in New College and a Royal Society University Research Fellow. Her research focuses on quantum device control. She develops machine learning algorithms for the automation of quantum device measurement and optimisation. She also harnesses the capabilities of nanoscale devices to explore thermodynamics in the quantum realm. She completed her PhD thesis at Université Grenoble Alpes, France, and her undergraduates’ studies at Universidad de Buenos Aires, Argentina.
Venue - Dates: AI4SD Network+ Conference, Chilworth Manor , Southampton, United Kingdom, 2022-03-01 - 2022-03-03

Identifiers

Local EPrints ID: 468643
URI: http://eprints.soton.ac.uk/id/eprint/468643
PURE UUID: be63282b-bdf8-4d84-b7e5-f4db4e70a9ad
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X

Catalogue record

Date deposited: 19 Aug 2022 16:36
Last modified: 20 Aug 2022 01:54

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Contributors

Author: Natalia Ares
Editor: Jeremy G. Frey ORCID iD
Editor: Samantha Kanza ORCID iD
Editor: Mahesan Niranjan ORCID iD

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