Adaptive Quantum Process Tomography via Linear Regression Estimation
Author(s)
Yu, Q
Dong, D
Wang, Y
Petersen, IR
Griffith University Author(s)
Year published
2020
Metadata
Show full item recordAbstract
This paper proposes a recursively adaptive tomography protocol to improve the precision of quantum process estimation for finite dimensional systems. The problem of quantum process tomography is firstly formulated as a parameter estimation problem which can then be solved by the linear regression estimation method. An adaptive algorithm is proposed for the selection of subsequent input states given the previous estimation results. Numerical results show that the proposed adaptive process tomography protocol can achieve an improved level of estimation performance.This paper proposes a recursively adaptive tomography protocol to improve the precision of quantum process estimation for finite dimensional systems. The problem of quantum process tomography is firstly formulated as a parameter estimation problem which can then be solved by the linear regression estimation method. An adaptive algorithm is proposed for the selection of subsequent input states given the previous estimation results. Numerical results show that the proposed adaptive process tomography protocol can achieve an improved level of estimation performance.
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Conference Title
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Subject
Computation Theory and Mathematics