多参数量子态估计问题的Bayesian Nagaoka-Hayashi界

Jun Suzuki
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引用次数: 1

摘要

在这项工作中,我们提出了一个贝叶斯版本的Nagaoka-Hayashi界,用于估计量子态的参数族。这个下界是将最近提出的点估计的下界推广到贝叶斯估计。然后,我们证明了所提出的下界可以作为半定规划问题有效地计算。作为下界,我们还从bayes Nagaoka-Hayashi界导出了holevo型界的bayes版本。最后,我们证明了新的下界比贝叶斯量子Cramer-Rao界更严格。
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Bayesian Nagaoka-Hayashi Bound for Multiparameter Quantum-State Estimation Problem
In this work we propose a Bayesian version of the Nagaoka-Hayashi bound when estimating a parametric family of quantum states. This lower bound is a generalization of a recently proposed bound for point estimation to Bayesian estimation. We then show that the proposed lower bound can be efficiently computed as a semidefinite programming problem. As a lower bound, we also derive a Bayesian version of the Holevo-type bound from the Bayesian Nagaoka-Hayashi bound. Lastly, we prove that the new lower bound is tighter than the Bayesian quantum Cramer-Rao bounds.
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来源期刊
Ieice Transactions on Fundamentals of Electronics Communications and Computer Sciences
Ieice Transactions on Fundamentals of Electronics Communications and Computer Sciences COMPUTER SCIENCE, HARDWARE & ARCHITECTURE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
1.10
自引率
20.00%
发文量
0
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