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Learning to calibrate quantum control pulses by iterative deconvolution

Learning to calibrate quantum control pulses by iterative deconvolution
Learning to calibrate quantum control pulses by iterative deconvolution
In experimental control of quantum systems, the precision is often hindered by imperfect applied electronics that distort pulses delivered to target quantum devices. To mitigate such error, the deconvolution method is commonly used for compensating the distortion via a convolutional model. However, its effectiveness is limited by model inaccuracies (e.g., imprecise parameters or unmodeled distortion dynamics). In this article, we propose a learning-based scheme to eliminate the residual calibration error by repeatedly applying the deconvolution operations. The resulting iterative deconvolution method is shown by simulation examples to be able to correct both linear and nonlinear model errors to the highest precision allowed by available finite sampling rates, and the intersampling error caused by finite sampling rate can be suppressed by actively introducing nonlinear components in the control electronics. The proposed method is also experimentally applied on a superconducting platform, which demonstrates improved performance than the noniterative deconvolution methods.
1063-6536
Cao, Xi
f5c49386-c5c3-46bc-ae50-1619549dd677
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Peng, Zhihui
481b5ff8-1892-4e9e-92cd-33b3650a72c0
Liu, Yuxi
d15b437c-9546-4b90-a3ca-e7621a8933ea
Wu, Rebing
c795bd63-150b-4a59-b9d1-1a250a0cc5af
Cao, Xi
f5c49386-c5c3-46bc-ae50-1619549dd677
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Peng, Zhihui
481b5ff8-1892-4e9e-92cd-33b3650a72c0
Liu, Yuxi
d15b437c-9546-4b90-a3ca-e7621a8933ea
Wu, Rebing
c795bd63-150b-4a59-b9d1-1a250a0cc5af

Cao, Xi, Chu, Bing, Peng, Zhihui, Liu, Yuxi and Wu, Rebing (2021) Learning to calibrate quantum control pulses by iterative deconvolution. IEEE Transactions on Control Systems Technology, 30 (1). (doi:10.1109/TCST.2021.3060321).

Record type: Article

Abstract

In experimental control of quantum systems, the precision is often hindered by imperfect applied electronics that distort pulses delivered to target quantum devices. To mitigate such error, the deconvolution method is commonly used for compensating the distortion via a convolutional model. However, its effectiveness is limited by model inaccuracies (e.g., imprecise parameters or unmodeled distortion dynamics). In this article, we propose a learning-based scheme to eliminate the residual calibration error by repeatedly applying the deconvolution operations. The resulting iterative deconvolution method is shown by simulation examples to be able to correct both linear and nonlinear model errors to the highest precision allowed by available finite sampling rates, and the intersampling error caused by finite sampling rate can be suppressed by actively introducing nonlinear components in the control electronics. The proposed method is also experimentally applied on a superconducting platform, which demonstrates improved performance than the noniterative deconvolution methods.

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e-pub ahead of print date: 5 March 2021

Identifiers

Local EPrints ID: 469274
URI: http://eprints.soton.ac.uk/id/eprint/469274
ISSN: 1063-6536
PURE UUID: e15bbf00-a080-4267-b782-20992e59390a
ORCID for Bing Chu: ORCID iD orcid.org/0000-0002-2711-8717

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Date deposited: 12 Sep 2022 16:43
Last modified: 17 Mar 2024 03:28

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Contributors

Author: Xi Cao
Author: Bing Chu ORCID iD
Author: Zhihui Peng
Author: Yuxi Liu
Author: Rebing Wu

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