A Two-Stage Solution to Quantum Process Tomography: Error Analysis and Optimal Design

Quantum process tomography is a critical task for characterizing the dynamics of quantum systems and achieving precise quantum control. In this paper, we propose a two-stage solution for both trace-preserving and non-trace-preserving quantum process tomography. Utilizing a tensor structure, our algorithm exhibits a computational complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(MLd^{2})$ </tex-math></inline-formula> where d is the dimension of the quantum system and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M, L~(M\geq d^{2}, L\geq d^{2})$ </tex-math></inline-formula> represent the numbers of different input states and measurement operators, respectively. We establish an analytical error upper bound and then design the optimal input states and the optimal measurement operators, which are both based on minimizing the error upper bound and maximizing the robustness characterized by the condition number. Numerical examples and testing on IBM quantum devices are presented to demonstrate the performance and efficiency of our algorithm.

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Publication Details

Journal
IEEE Transactions on Information Theory
Published
2024-12-24
DOI
https://doi.org/10.1109/tit.2024.3522005
Primary Topic
Quantum Information and Cryptography
Type
article
Field-Weighted Citation Impact
0.00

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article

A Two-Stage Solution to Quantum Process Tomography: Error Analysis and Optimal Design

Daoyi Dong, Ian R. Petersen, Jun Zhang, Shuixin Xiao et al.
IEEE Transactions on Information Theory
Quantum Information and Cryptography
article

A Two-Stage Solution to Quantum Process Tomography: Error Analysis and Optimal Design

Daoyi Dong, Ian R. Petersen, Jun Zhang, Shuixin Xiao, Gary J. Mooney, Hidehiro Yonezawa, Yuanlong Wang
article en

Abstract

Quantum process tomography is a critical task for characterizing the dynamics of quantum systems and achieving precise quantum control. In this paper, we propose a two-stage solution for both trace-preserving and non-trace-preserving quantum process tomography. Utilizing a tensor structure, our algorithm exhibits a computational complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(MLd^{2})$ </tex-math></inline-formula> where d is the dimension of the quantum system and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M, L~(M\geq d^{2}, L\geq d^{2})$ </tex-math></inline-formula> represent the numbers of different input states and measurement operators, respectively. We establish an analytical error upper bound and then design the optimal input states and the optimal measurement operators, which are both based on minimizing the error upper bound and maximizing the robustness characterized by the condition number. Numerical examples and testing on IBM quantum devices are presented to demonstrate the performance and efficiency of our algorithm.

IEEE Transactions on Information TheoryVol. 71(3)
Australian National University (AU), The University of Melbourne (AU), Shanghai Jiao Tong University (CN), University of Canberra (AU), Chinese Academy of Sciences (CN), UNSW Sydney (AU), Academy of Mathematics and Systems Science (CN), RIKEN Center for Advanced Photonics (JP), RIKEN Center for Quantum Computing
National Natural Science Foundation of China
Openalex Percentile: Top 78%
Quantum Information and Cryptography
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