Quantum Hamiltonian Identifiability via a Similarity Transformation Approach and Beyond
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Dong, Daoyi
Sone, Akira
Petersen, Ian R
Yonezawa, Hidehiro
Cappellaro, Paola
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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 single-qubit 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.
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IEEE Transactions on Automatic Control
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65
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11
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© The Author(s) 2020. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Applied mathematics
Electronics, sensors and digital hardware
Mechanical engineering
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Automation & Control Systems
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Wang, Y; Dong, D; Sone, A; Petersen, IR; Yonezawa, H; Cappellaro, P, Quantum Hamiltonian Identifiability via a Similarity Transformation Approach and Beyond, IEEE Transactions on Automatic Control, 2020, 65 (11), pp. 4632-4647