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Design of Feedback Control Laws for Information Transfer in Spintronics Networks

Sophie Shermer Orcid Logo, Edmond A. Jonckheere, Frank C. Langbein

IEEE Transactions on Automatic Control, Volume: 63, Issue: 8, Pages: 2523 - 2536

Swansea University Author: Sophie Shermer Orcid Logo

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DOI (Published version): 10.1109/tac.2017.2777187

Abstract

Information encoded in networks of stationary, interacting spin-1/2 particles is central for many applications ranging from quantum spintronics to quantum information processing. Without control, however, information transfer through such networks is generally inefficient. \new{Currently available c...

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Published in: IEEE Transactions on Automatic Control
ISSN: 0018-9286 2334-3303
Published: Institute of Electrical and Electronics Engineers (IEEE) 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa37100
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spelling 2020-07-28T13:34:13.4632106 v2 37100 2017-11-27 Design of Feedback Control Laws for Information Transfer in Spintronics Networks 6ebef22eb31eafc75aedcf5bfe487777 0000-0002-5530-7750 Sophie Shermer Sophie Shermer true false 2017-11-27 SPH Information encoded in networks of stationary, interacting spin-1/2 particles is central for many applications ranging from quantum spintronics to quantum information processing. Without control, however, information transfer through such networks is generally inefficient. \new{Currently available control methods to maximize the transfer fidelities and speeds mainly rely on dynamic control using time-varying fields and often assume instantaneous readout. We present an alternative approach to achieving} efficient, high-fidelity transfer of excitations by shaping the energy landscape via the design of time-invariant feedback control laws without recourse to dynamic control. \new{Both instantaneous readout and the more realistic case of finite readout windows are considered. The technique can also be used to freeze information by designing energy landscapes that achieve Anderson localization.} Perfect state or super-optimal transfer and localization are enabled by conditions on the eigenstructure of the system and signature properties for the eigenvectors. Given the eigenstructure enabled by super-optimality, it is shown that feedback controllers that achieve perfect state transfer are, surprisingly, also the most robust with regard to uncertainties in the system and control parameters. Journal Article IEEE Transactions on Automatic Control 63 8 2523 2536 Institute of Electrical and Electronics Engineers (IEEE) 0018-9286 2334-3303 quantum systems, model-based feedback control, spin systems 1 8 2018 2018-08-01 10.1109/tac.2017.2777187 COLLEGE NANME Physics COLLEGE CODE SPH Swansea University 2020-07-28T13:34:13.4632106 2017-11-27T17:05:29.3877879 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Physics Sophie Shermer 0000-0002-5530-7750 1 Edmond A. Jonckheere 2 Frank C. Langbein 3 0037100-27112017170940.pdf 1607.05294v3.pdf 2017-11-27T17:09:40.6830000 Output 1544293 application/pdf Accepted Manuscript true 2017-11-27T00:00:00.0000000 true eng
title Design of Feedback Control Laws for Information Transfer in Spintronics Networks
spellingShingle Design of Feedback Control Laws for Information Transfer in Spintronics Networks
Sophie Shermer
title_short Design of Feedback Control Laws for Information Transfer in Spintronics Networks
title_full Design of Feedback Control Laws for Information Transfer in Spintronics Networks
title_fullStr Design of Feedback Control Laws for Information Transfer in Spintronics Networks
title_full_unstemmed Design of Feedback Control Laws for Information Transfer in Spintronics Networks
title_sort Design of Feedback Control Laws for Information Transfer in Spintronics Networks
author_id_str_mv 6ebef22eb31eafc75aedcf5bfe487777
author_id_fullname_str_mv 6ebef22eb31eafc75aedcf5bfe487777_***_Sophie Shermer
author Sophie Shermer
author2 Sophie Shermer
Edmond A. Jonckheere
Frank C. Langbein
format Journal article
container_title IEEE Transactions on Automatic Control
container_volume 63
container_issue 8
container_start_page 2523
publishDate 2018
institution Swansea University
issn 0018-9286
2334-3303
doi_str_mv 10.1109/tac.2017.2777187
publisher Institute of Electrical and Electronics Engineers (IEEE)
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 Information encoded in networks of stationary, interacting spin-1/2 particles is central for many applications ranging from quantum spintronics to quantum information processing. Without control, however, information transfer through such networks is generally inefficient. \new{Currently available control methods to maximize the transfer fidelities and speeds mainly rely on dynamic control using time-varying fields and often assume instantaneous readout. We present an alternative approach to achieving} efficient, high-fidelity transfer of excitations by shaping the energy landscape via the design of time-invariant feedback control laws without recourse to dynamic control. \new{Both instantaneous readout and the more realistic case of finite readout windows are considered. The technique can also be used to freeze information by designing energy landscapes that achieve Anderson localization.} Perfect state or super-optimal transfer and localization are enabled by conditions on the eigenstructure of the system and signature properties for the eigenvectors. Given the eigenstructure enabled by super-optimality, it is shown that feedback controllers that achieve perfect state transfer are, surprisingly, also the most robust with regard to uncertainties in the system and control parameters.
published_date 2018-08-01T03:46:37Z
_version_ 1763752224579649536
score 11.012678

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