Implementing Graph-Theoretic Feature Selection by Quantum Approximate Optimization Algorithm-Reference-Cited by-同舟云学术

Implementing Graph-Theoretic Feature Selection by Quantum Approximate Optimization Algorithm

Published:2022 Issue: Volume: Page:1-14
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Li YaoChong¹^ORCID,Zhou Ri-Gui¹^ORCID,Xu RuiQing¹^ORCID,Luo Jia¹^ORCID,Hu WenWen¹,Fan Ping²^ORCID

Affiliation:

1. Research Center of Intelligent Information Processing and Quantum Intelligent Computing and the College of Information Engineering, Shanghai Maritime University, Shanghai, China

2. College of Information Engineering, East China Jiaotong University, Nanchang, China

Funder

National Key Research and Development Plan

National Natural Science Foundation of China

Shanghai Science and Technology Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/09836975.pdf?arnumber=9836975

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