Quantum machine learning on near-term quantum devices: Current state of supervised and unsupervised techniques for real-world applications-Reference-Cited by-同舟云学术

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Quantum machine learning on near-term quantum devices: Current state of supervised and unsupervised techniques for real-world applications

Published:2024-06-04 Issue:6 Volume:21 Page:
ISSN:2331-7019
Container-title:Physical Review Applied
language:en
Short-container-title:Phys. Rev. Applied

Author:

Gujju Yaswitha¹^ORCID,Matsuo Atsushi²^ORCID,Raymond Rudy¹

Affiliation:

1. Department of Computer Science, University of Tokyo 4-6-1, Shirokanedai, Minato-ku, Tokyo 108-8639, Japan

2. IBM Quantum, IBM Research – Tokyo, 19–21 Nihonbashi Hakozaki-cho, Chuo-ku, Tokyo, 103-8510, Japan

Funder

Kakenhi

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevApplied.21.067001/fulltext

Reference291 articles.

1. Quantum Computing in the NISQ era and beyond

2. Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer

3. Experimental realization of Shor's quantum factoring algorithm using nuclear magnetic resonance

4. Quantum Computation and Quantum Information

Cited by 1 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. Quantum Supervised Learning;KI - Künstliche Intelligenz;2024-07-19

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