Benchmarking quantum tomography completeness and fidelity with machine learning-Reference-Cited by-同舟云学术

Benchmarking quantum tomography completeness and fidelity with machine learning

Published:2021-10-01 Issue:10 Volume:23 Page:103021
ISSN:1367-2630
Container-title:New Journal of Physics
language:
Short-container-title:New J. Phys.

Author:

Teo Yong Siah^ORCID,Shin Seongwook,Jeong Hyunseok,Kim Yosep,Kim Yoon-Ho,Struchalin Gleb I^ORCID,Kovlakov Egor V,Straupe Stanislav S^ORCID,Kulik Sergei P,Leuchs Gerd^ORCID,Sánchez-Soto Luis L^ORCID

Funder

Center of Excellence <<Center of Photonics>> funded by the Ministry of Science and Higher Education of the Russian Federation

Ministerio de Ciencia e Innovaci{\'o}n

European Union's Horizon 2020 research and innovation program

ITRC support program

Institute of Information & Communications Technology Planning & Evaluation

Russian Foundation for Basic Research

National Research Foundation of Korea

Russian National Technological Initiative via MSU Quantum Technology Centre

Publisher

IOP Publishing

Subject

General Physics and Astronomy

Link

https://iopscience.iop.org/article/10.1088/1367-2630/ac1fcb/pdf

Reference102 articles.

1. An adaptive variational algorithm for exact molecular simulations on a quantum computer

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4. Supervised learning with quantum-enhanced feature spaces

5. Training deep quantum neural networks

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