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Iterative Phase Optimization of Elementary Quantum Error Correcting Codes

Markus Muller, A. Rivas, E. A. Martínez, D. Nigg, P. Schindler, T. Monz, R. Blatt, M. A. Martin-Delgado

Physical Review X, Volume: 6, Issue: 3, Pages: 031030-1 - 031030-12

Swansea University Author: Markus Muller

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DOI (Published version): 10.1103/physrevx.6.031030

Abstract

Performing experiments on small-scale quantum computers is certainly a challenging endeavor. Many parameters need to be optimized to achieve high-fidelity operations. This can be done efficiently for operations acting on single qubits, as errors can be fully characterized. For multiqubit operations,...

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Published in: Physical Review X
ISSN: 2160-3308
Published: American Physical Society (APS) 2016
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URI: https://cronfa.swan.ac.uk/Record/cronfa33683
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spelling 2020-07-29T16:25:59.8225961 v2 33683 2017-05-16 Iterative Phase Optimization of Elementary Quantum Error Correcting Codes 9b2ac559af27c967ece69db08b83762a Markus Muller Markus Muller true false 2017-05-16 FGSEN Performing experiments on small-scale quantum computers is certainly a challenging endeavor. Many parameters need to be optimized to achieve high-fidelity operations. This can be done efficiently for operations acting on single qubits, as errors can be fully characterized. For multiqubit operations, though, this is no longer the case, as in the most general case, analyzing the effect of the operation on the system requires a full state tomography for which resources scale exponentially with the system size. Furthermore, in recent experiments, additional electronic levels beyond the two-level system encoding the qubit have been used to enhance the capabilities of quantum-information processors, which additionally increases the number of parameters that need to be controlled. For the optimization of the experimental system for a given task (e.g., a quantum algorithm), one has to find a satisfactory error model and also efficient observables to estimate the parameters of the model. In this manuscript, we demonstrate a method to optimize the encoding procedure for a small quantum error correction code in the presence of unknown but constant phase shifts. The method, which we implement here on a small-scale linear ion-trap quantum computer, is readily applicable to other AMO platforms for quantum-information processing. Journal Article Physical Review X 6 3 031030-1 031030-12 American Physical Society (APS) 2160-3308 Quantum Computing, Quantum Optimisation, Trapped Ions, Quantum Error Correction 24 8 2016 2016-08-24 10.1103/physrevx.6.031030 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University 2020-07-29T16:25:59.8225961 2017-05-16T18:41:40.7871399 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Physics Markus Muller 1 A. Rivas 2 E. A. Martínez 3 D. Nigg 4 P. Schindler 5 T. Monz 6 R. Blatt 7 M. A. Martin-Delgado 8 0033683-06072017152845.pdf PhysRevXv2.pdf 2017-07-06T15:28:45.1370000 Output 1626679 application/pdf Version of Record true This article is available under the terms of the Creative Commons Attribution 3.0 License. true eng http://creativecommons.org/licenses/by/3.0/
title Iterative Phase Optimization of Elementary Quantum Error Correcting Codes
spellingShingle Iterative Phase Optimization of Elementary Quantum Error Correcting Codes
Markus Muller
title_short Iterative Phase Optimization of Elementary Quantum Error Correcting Codes
title_full Iterative Phase Optimization of Elementary Quantum Error Correcting Codes
title_fullStr Iterative Phase Optimization of Elementary Quantum Error Correcting Codes
title_full_unstemmed Iterative Phase Optimization of Elementary Quantum Error Correcting Codes
title_sort Iterative Phase Optimization of Elementary Quantum Error Correcting Codes
author_id_str_mv 9b2ac559af27c967ece69db08b83762a
author_id_fullname_str_mv 9b2ac559af27c967ece69db08b83762a_***_Markus Muller
author Markus Muller
author2 Markus Muller
A. Rivas
E. A. Martínez
D. Nigg
P. Schindler
T. Monz
R. Blatt
M. A. Martin-Delgado
format Journal article
container_title Physical Review X
container_volume 6
container_issue 3
container_start_page 031030-1
publishDate 2016
institution Swansea University
issn 2160-3308
doi_str_mv 10.1103/physrevx.6.031030
publisher American Physical Society (APS)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Physics{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Physics
document_store_str 1
active_str 0
description Performing experiments on small-scale quantum computers is certainly a challenging endeavor. Many parameters need to be optimized to achieve high-fidelity operations. This can be done efficiently for operations acting on single qubits, as errors can be fully characterized. For multiqubit operations, though, this is no longer the case, as in the most general case, analyzing the effect of the operation on the system requires a full state tomography for which resources scale exponentially with the system size. Furthermore, in recent experiments, additional electronic levels beyond the two-level system encoding the qubit have been used to enhance the capabilities of quantum-information processors, which additionally increases the number of parameters that need to be controlled. For the optimization of the experimental system for a given task (e.g., a quantum algorithm), one has to find a satisfactory error model and also efficient observables to estimate the parameters of the model. In this manuscript, we demonstrate a method to optimize the encoding procedure for a small quantum error correction code in the presence of unknown but constant phase shifts. The method, which we implement here on a small-scale linear ion-trap quantum computer, is readily applicable to other AMO platforms for quantum-information processing.
published_date 2016-08-24T03:41:43Z
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score 11.012678

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