Iterative layerwise training for the quantum approximate optimization algorithm-Reference-Cited by-同舟云学术

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Iterative layerwise training for the quantum approximate optimization algorithm

Published:2024-05-02 Issue:5 Volume:109 Page:
ISSN:2469-9926
Container-title:Physical Review A
language:en
Short-container-title:Phys. Rev. A

Author:

Lee Xinwei¹^ORCID,Yan Xinjian²^ORCID,Xie Ningyi²,Cai Dongsheng³,Saito Yoshiyuki⁴^ORCID,Asai Nobuyoshi⁵

Affiliation:

1. Graduate School of Systems and Information Engineering, University of Tsukuba, Ibaraki Prefecture 305-8577, Japan

2. Graduate School of Science and Technology, University of Tsukuba, Ibaraki Prefecture 305-8577, Japan

3. Faculty of Engineering, Information and Systems, University of Tsukuba, Ibaraki Prefecture 305-8577, Japan

4. Graduate School of Computer Science and Engineering, University of Aizu, Fukushima Prefecture 965-0006, Japan

5. School of Computer Science and Engineering, University of Aizu, Fukushima Prefecture 965-0006, Japan

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevA.109.052406/fulltext

Reference18 articles.

1. Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices

2. To quantum or not to quantum: towards algorithm selection in near-term quantum optimization

3. Local classical MAX-CUT algorithm outperforms p=2 QAOA on high-girth regular graphs

4. From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz

5. Adaptive quantum approximate optimization algorithm for solving combinatorial problems on a quantum computer

Cited by 2 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. Near-term quantum algorithm for solving the MaxCut problem with fewer quantum resources;Physica A: Statistical Mechanics and its Applications;2024-08

2. A Layerwise-Multi-Angle Approach to Fine-Tuning the Quantum Approximate Optimization Algorithm;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

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