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Title: Variational quantum algorithms to estimate rank, quantum entropies, fidelity and Fisher information via purity minimization
Authors: Tan, Kok Chuan
Volkoff, Tyler
Keywords: Science::Physics
Issue Date: 2021
Source: Tan, K. C. & Volkoff, T. (2021). Variational quantum algorithms to estimate rank, quantum entropies, fidelity and Fisher information via purity minimization. Physical Review Research, 3(3), 033251-1-033251-9.
Journal: Physical Review Research
Abstract: Variational quantum algorithms (VQAs) that estimate values of widely used physical quantities such as the rank, quantum entropies, the Bures fidelity and the quantum Fisher information of mixed quantum states are developed. In addition, variations of these VQAs are also adapted to perform other useful functions such as quantum state learning and approximate fractional inverses. The common theme shared by the proposed algorithms is that their cost functions are all based on minimizing the quantum purity of a quantum state. Strategies to mitigate or avoid the problem of exponentially vanishing cost function gradients are also discussed.
ISSN: 2643-1564
DOI: 10.1103/PhysRevResearch.3.033251
Schools: School of Physical and Mathematical Sciences 
Rights: © 2021 the Authors. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the authors and the published article’s title, journal citation, and DOI.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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