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Designing High-Fidelity Single-Shot Three-Qubit Gates: A Machine-Learning Approach

Zahedinejad, Ehsan and Ghosh, Joydip and Sanders, Barry C. (2016) Designing High-Fidelity Single-Shot Three-Qubit Gates: A Machine-Learning Approach. Physical Review Applied, 6 (5). Art. No. 054005. ISSN 2331-7019. http://resolver.caltech.edu/CaltechAUTHORS:20161117-074832839

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Abstract

Three-qubit quantum gates are key ingredients for quantum error correction and quantum-information processing. We generate quantum-control procedures to design three types of three-qubit gates, namely Toffoli, controlled-NOT-NOT, and Fredkin gates. The design procedures are applicable to a system comprising three nearest-neighbor-coupled superconducting artificial atoms. For each three-qubit gate, the numerical simulation of the proposed scheme achieves 99.9% fidelity, which is an accepted threshold fidelity for fault-tolerant quantum computing. We test our procedure in the presence of decoherence-induced noise and show its robustness against random external noise generated by the control electronics. The three-qubit gates are designed via the machine-learning algorithm called subspace-selective self-adaptive differential evolution.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1103/PhysRevApplied.6.054005DOIArticle
http://journals.aps.org/prapplied/abstract/10.1103/PhysRevApplied.6.054005PublisherArticle
https://arxiv.org/abs/1511.08862arXivDiscussion Paper
ORCID:
AuthorORCID
Sanders, Barry C.0000-0002-8326-8912
Additional Information:© 2016 American Physical Society. Received 10 December 2015; revised manuscript received 27 September 2016; published 16 November 2016. This research was partially funded by NSERC and Alberta Innovates. E. Z. appreciates the financial support from MITACS. J. G. acknowledges the financial support from the University of Calgary’s Eyes High Program and was also supported by ONR under Grant No. N00014-15-1-0029, and by the NSF under Grant No. PHY-1104660. B. C. S. appreciates the financial support provided by China’s 1000 Talent Plan and by the Institute for Quantum Information and Matter, which is a National Science Foundation Physics Frontiers Center (NSF Grant No. PHY-1125565) with the support of the Gordon and Betty Moore Foundation (Grant No. GBMF-2644). This research was enabled in part by support provided by WestGrid and Compute Canada Calcul Canada. We thank Alexandre Blais, Austin Fowler, Michael Geller, and Simon Nigg for the valuable discussions and Jonathan Johannes for proofreading the manuscript.
Group:IQIM, Institute for Quantum Information and Matter
Funders:
Funding AgencyGrant Number
Natural Sciences and Engineering Research Council of Canada (NSERC)UNSPECIFIED
Mathematics of Information Technology and Complex Systems (MITACS)UNSPECIFIED
University of CalgaryUNSPECIFIED
Office of Naval Research (ONR)N00014-15-1-0029
NSFPHY-1104660
China 1000 Talent PlanUNSPECIFIED
Institute for Quantum Information and Matter (IQIM)UNSPECIFIED
NSFPHY-1125565
Gordon and Betty Moore FoundationGBMF-2644
Record Number:CaltechAUTHORS:20161117-074832839
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20161117-074832839
Official Citation:Designing High-Fidelity Single-Shot Three-Qubit Gates: A Machine-Learning Approach Ehsan Zahedinejad, Joydip Ghosh, and Barry C. Sanders Phys. Rev. Applied 6, 054005
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:72091
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:17 Nov 2016 19:41
Last Modified:20 Nov 2017 18:51

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