Extreme expected values and their applications in quantum information processing
Wangjun Lu, Lei Shao, Xingyu Zhang, Zhucheng Zhang, Jie Chen, Hong, Tao, and Xiaoguang Wang

TL;DR
This paper develops a method to find extreme expected values of functions under constraints and applies it to optimize quantum information processing tasks, including quantum Fisher information and energy storage in quantum systems.
Contribution
The paper introduces a splitting method for monotonic functions to determine maximum and minimum expected values, with applications to quantum parameter estimation and quantum batteries.
Findings
Identified optimal input states for quantum Fisher information in interferometry.
Proved NOON state minimizes quantum Fisher information in certain conditions.
Determined optimal initial states for maximum energy and power in quantum batteries.
Abstract
We consider the probability distribution when the monotonic function of the independent variable takes the maximum or minimum expected value under the two constraints of a certain probability and a certain expected value of the independent variable . We proposed an equal probability and equal expected value splitting method. With this method, we proved four inequalities, and two of them can be reduced to Jensen's inequalities. Subsequently, we find that after dividing the non-monotone function into multiple monotone intervals, the problem of solving the maximum and minimum expected values of can be transformed into the problem of solving the extreme value of a multiple-variable function. Finally, we apply the proved theory to solve three problems in quantum information processing. When studying the quantum parameter estimation in Mach-Zehnder interferometer,…
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Taxonomy
TopicsQuantum Information and Cryptography · Quantum Computing Algorithms and Architecture · Quantum Mechanics and Applications
