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Data-driven gradient algorithm for high-precision quantum control

Data-driven gradient algorithm for high-precision quantum control
Data-driven gradient algorithm for high-precision quantum control
In the quest to achieve scalable quantum information processing technologies, gradient-based optimal control algorithms (e.g., grape) are broadly used for implementing high-precision quantum gates, but their performance is often hindered by deterministic or random errors in the system model and the control electronics. In this paper, we show that grape can be taught to be more effective by jointly learning from the design model and the experimental data obtained from process tomography. The resulting data-driven gradient optimization algorithm (d-grape) can in principle correct all deterministic gate errors, with a mild efficiency loss. The d-grape algorithm may become more powerful with broadband controls that involve a large number of control parameters, while other algorithms usually slow down due to the increased size of the search space. These advantages are demonstrated by simulating the implementation of a two-qubit controlled-not gate.
1050-2947
Wu, Rebing
b1e55f53-fa11-4229-a346-e0499f6747e1
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Owens, D
85974860-3700-4863-9cf0-2edc3e75e2c9
Rabitz, Herschel
3e81aa62-a017-448f-a82a-8cf03df52899
Wu, Rebing
b1e55f53-fa11-4229-a346-e0499f6747e1
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Owens, D
85974860-3700-4863-9cf0-2edc3e75e2c9
Rabitz, Herschel
3e81aa62-a017-448f-a82a-8cf03df52899

Wu, Rebing, Chu, Bing, Owens, D and Rabitz, Herschel (2018) Data-driven gradient algorithm for high-precision quantum control. Physical Review A, 97 (4), [42122]. (doi:10.1103/PhysRevA.97.042122).

Record type: Article

Abstract

In the quest to achieve scalable quantum information processing technologies, gradient-based optimal control algorithms (e.g., grape) are broadly used for implementing high-precision quantum gates, but their performance is often hindered by deterministic or random errors in the system model and the control electronics. In this paper, we show that grape can be taught to be more effective by jointly learning from the design model and the experimental data obtained from process tomography. The resulting data-driven gradient optimization algorithm (d-grape) can in principle correct all deterministic gate errors, with a mild efficiency loss. The d-grape algorithm may become more powerful with broadband controls that involve a large number of control parameters, while other algorithms usually slow down due to the increased size of the search space. These advantages are demonstrated by simulating the implementation of a two-qubit controlled-not gate.

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More information

Accepted/In Press date: 11 April 2018
e-pub ahead of print date: 24 April 2018
Published date: April 2018

Identifiers

Local EPrints ID: 419687
URI: http://eprints.soton.ac.uk/id/eprint/419687
ISSN: 1050-2947
PURE UUID: 722eaf21-734f-4b68-8b1a-1642eae90ab1
ORCID for Bing Chu: ORCID iD orcid.org/0000-0002-2711-8717

Catalogue record

Date deposited: 19 Apr 2018 16:30
Last modified: 27 Apr 2022 01:56

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Contributors

Author: Rebing Wu
Author: Bing Chu ORCID iD
Author: D Owens
Author: Herschel Rabitz

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