Toward Robust Autotuning of Noisy Quantum dot Devices

Ziegler, Joshua; McJunkin, Thomas; Joseph, E. S.; Kalantre, Sandesh S.; Harpt, Benjamin; Savage, D. E.; Lagally, M. G.; Eriksson, M. A.; Taylor, Jacob M.; Zwolak, Justyna P.

doi:10.1103/physrevapplied.17.024069

Toward Robust Autotuning of Noisy Quantum dot Devices

Journal Article · Thu Feb 24 23:00:00 EST 2022 · Physical Review Applied

DOI:https://doi.org/10.1103/physrevapplied.17.024069· OSTI ID:1979645

^[1]; ^[2]; ^[3]; Kalantre, Sandesh S. ^[4]; Harpt, Benjamin ^[3]; ^[3]; Lagally, M. G. ^[3]; Eriksson, M. A. ^[3]; Taylor, Jacob M. ^[5]; ^[6]

National Inst. of Standards and Technology (NIST), Gaithersburg, MD (United States); OSTI
National Inst. of Standards and Technology (NIST), Gaithersburg, MD (United States); Univ. of Wisconsin, Madison, WI (United States)
Univ. of Wisconsin, Madison, WI (United States)
Univ. of Maryland, College Park, MD (United States). Joint Quantum Inst.; Univ. of Maryland, College Park, MD (United States). Joint Center for Quantum Information and Computer Science
National Inst. of Standards and Technology (NIST), Gaithersburg, MD (United States); Univ. of Maryland, College Park, MD (United States). Joint Quantum Inst.; Univ. of Maryland, College Park, MD (United States). Joint Center for Quantum Information and Computer Science
National Inst. of Standards and Technology (NIST), Gaithersburg, MD (United States)

The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy or otherwise low-quality data is processed by an autonomous system. In this work, we propose a framework for robust autotuning of QD devices that combines a machine learning (ML) state classifier with a data quality control module. The data quality control module acts as a “gatekeeper” system, ensuring that only reliable data are processed by the state classifier. Lower data quality results in either device recalibration or termination. To train both ML systems, we enhance the QD simulation by incorporating synthetic noise typical of QD experiments. We confirm that the inclusion of synthetic noise in the training of the state classifier significantly improves the performance, resulting in an accuracy of 95.0(9)% when tested on experimental data. We then validate the functionality of the data quality control module by showing that the state classifier performance deteriorates with decreasing data quality, as expected. Our results establish a robust and flexible ML framework for autonomous tuning of noisy QD devices.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of Wisconsin, Madison, WI (United States)

Sponsoring Organization:: USDOE Office of Science (SC); US Army Research Office (ARO); National Science Foundation (NSF)

Grant/Contract Number:: FG02-03ER46028; FG02-03ER46028

OSTI ID:: 1979645

Alternate ID(s):: OSTI ID: 1888318

Journal Information:: Physical Review Applied, Journal Name: Physical Review Applied Journal Issue: 2 Vol. 17; ISSN 2331-7019

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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75 CONDENSED MATTER PHYSICS
SUPERCONDUCTIVITY AND SUPERFLUIDITY
Physics
double quantum dots
machine learning
noise
quantum computation
quantum control
quantum information architectures & platforms
quantum information with solid state qubits
semiconductor compounds

Toward Robust Autotuning of Noisy Quantum dot Devices

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