Quantum Hamiltonian Identifiability via a Similarity Transformation Approach and beyond
dc.contributor.author | Wang, Yuanlong | |
dc.contributor.author | Dong, Daoyi | |
dc.contributor.author | Sone, Akira | |
dc.contributor.author | Petersen, Ian | |
dc.contributor.author | Yonezawa, Hidehiro | |
dc.contributor.author | Cappellaro, Paola | |
dc.date.accessioned | 2023-08-09T22:48:13Z | |
dc.date.available | 2023-08-09T22:48:13Z | |
dc.date.issued | 2020 | |
dc.date.updated | 2022-07-24T08:18:04Z | |
dc.description.abstract | The identifiability of a system is concerned with whether the unknown parameters in the system can be uniquely determined with all the possible data generated by a certain experimental setting. A test of quantum Hamiltonian identifiability is an important tool to save time and cost when exploring the identification capability of quantum probes and experimentally implementing quantum identification schemes. In this article, we generalize the identifiability test based on the similarity transformation approach (STA) in classical control theory and extend it to the domain of quantum Hamiltonian identification. We employ the STA to prove the identifiability of spin-1/2 chain systems with arbitrary dimension assisted by singlequbit probes. We further extend the traditional STA method by proposing a structure preserving transformation (SPT method for nonminimal systems. We use the SPT method to introduce an indicator for the existence of economic quantum Hamiltonian identification algorithms, whose computational complexity directly depends on the number of unknown parameters (which could be much smaller than the system dimension). Finally, we give an example of such an economic Hamiltonian identification algorithm and perform simulations to demonstrate its effectiveness. | en_AU |
dc.format.mimetype | application/pdf | en_AU |
dc.identifier.issn | 0018-9286 | en_AU |
dc.identifier.uri | http://hdl.handle.net/1885/295477 | |
dc.language.iso | en_AU | en_AU |
dc.provenance | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ | en_AU |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE Inc) | en_AU |
dc.rights | © 2020 IEEE | en_AU |
dc.rights.license | Creative Commons Attribution License 4.0 | en_AU |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_AU |
dc.source | IEEE Transactions on Automatic Control | en_AU |
dc.subject | Hamiltonian identifiability | en_AU |
dc.subject | quantum Hamiltonian identification | en_AU |
dc.subject | quantum system | en_AU |
dc.subject | similarity transformation approach (STA) | en_AU |
dc.title | Quantum Hamiltonian Identifiability via a Similarity Transformation Approach and beyond | en_AU |
dc.type | Journal article | en_AU |
dcterms.accessRights | Open Access | en_AU |
local.bibliographicCitation.issue | 11 | en_AU |
local.bibliographicCitation.lastpage | 4647 | en_AU |
local.bibliographicCitation.startpage | 4632 | en_AU |
local.contributor.affiliation | Wang, Yuanlong, University of New South Wales | en_AU |
local.contributor.affiliation | Dong, Daoyi, University of New South Wales | en_AU |
local.contributor.affiliation | Sone, Akira, Research Laboratory of Electronics | en_AU |
local.contributor.affiliation | Petersen, Ian, College of Engineering and Computer Science, ANU | en_AU |
local.contributor.affiliation | Yonezawa, Hidehiro, The University of New South Wales | en_AU |
local.contributor.affiliation | Cappellaro, Paola, Research Laboratory of Electronics and the Department of Nuclear Science and Engineering | en_AU |
local.contributor.authoremail | u4036493@anu.edu.au | en_AU |
local.contributor.authoruid | Petersen, Ian, u4036493 | en_AU |
local.description.notes | Imported from ARIES | en_AU |
local.identifier.absfor | 400705 - Control engineering | en_AU |
local.identifier.absseo | 280110 - Expanding knowledge in engineering | en_AU |
local.identifier.ariespublication | a383154xPUB16186 | en_AU |
local.identifier.citationvolume | 65 | en_AU |
local.identifier.doi | 10.1109/TAC.2020.2973582 | en_AU |
local.identifier.scopusID | 2-s2.0-85094695813 | |
local.identifier.thomsonID | WOS:000583711500011 | |
local.identifier.uidSubmittedBy | a383154 | en_AU |
local.publisher.url | https://www.ieee.org/ | en_AU |
local.type.status | Published Version | en_AU |
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