Estimating the degree of non-Markovianity using machine learning-Reference-Cited by-同舟云学术

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Estimating the degree of non-Markovianity using machine learning

Published:2021-02-24 Issue:2 Volume:103 Page:
ISSN:2469-9926
Container-title:Physical Review A
language:en
Short-container-title:Phys. Rev. A

Author:

Fanchini Felipe F.,Karpat Göktuğ^ORCID,Rossatto Daniel Z.^ORCID,Norambuena Ariel^ORCID,Coto Raúl^ORCID

Funder

Fundação de Amparo à Pesquisa do Estado de São Paulo

Türkiye Bilimler Akademisi

Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

Fondo Nacional de Desarrollo Científico y Tecnológico

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevA.103.022425/fulltext

Reference76 articles.

1. Machine learning: Trends, perspectives, and prospects

2. Machine learning & artificial intelligence in the quantum domain: a review of recent progress

3. A high-bias, low-variance introduction to Machine Learning for physicists

4. Machine learning and the physical sciences

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1. Deep learning the hierarchy of steering measurement settings of qubit-pair states;Communications Physics;2024-03-01

2. Experimental graybox quantum system identification and control;npj Quantum Information;2024-01-13

3. Machine Learning based Noise Characterization and Correction on Neutral Atoms NISQ Devices;Advanced Quantum Technologies;2023-11-10

4. Quantum speed limit for time-fractional open systems;Chaos, Solitons & Fractals;2023-10

5. Non-Markovianity in the time evolution of open quantum systems assessed by means of quantum state distance;Physica Scripta;2023-07-25

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