Superpolynomial quantum-classical separation for density modeling-Reference-Cited by-同舟云学术

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Superpolynomial quantum-classical separation for density modeling

Published:2023-04-13 Issue:4 Volume:107 Page:
ISSN:2469-9926
Container-title:Physical Review A
language:en
Short-container-title:Phys. Rev. A

Author:

Pirnay Niklas^ORCID,Sweke Ryan,Eisert Jens^ORCID,Seifert Jean-Pierre^ORCID

Funder

Berlin University Alliance

Deutsche Forschungsgemeinschaft

Bundesministerium für Bildung und Forschung

Berlin Institute for the Foundations of Learning and Data

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevA.107.042416/fulltext

Reference20 articles.

1. Quantum machine learning

2. Machine learning and the physical sciences

3. Parameterized quantum circuits as machine learning models

4. Variational quantum algorithms

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Guided quantum compression for high dimensional data classification;Machine Learning: Science and Technology;2024-07-16

2. Let quantum neural networks choose their own frequencies;Physical Review A;2024-04-22

3. An in-principle super-polynomial quantum advantage for approximating combinatorial optimization problems via computational learning theory;Science Advances;2024-03-15

4. An isotropic zero thermal expansion alloy with super-high toughness;Nature Communications;2024-03-13

5. Understanding quantum machine learning also requires rethinking generalization;Nature Communications;2024-03-13

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