Quantum Approximate Optimization Algorithm for Graph Partitioning

被引：0
|
作者：
Yuan, Zhi-Qiang [1 ]
Yang, Si-Chun [1 ]
Ruan, Yue [1 ]
Xue, Xi-Ling [1 ]
Tao, Tao [1 ]
机构：
[1] School of Computer Science and Technology, Anhui University of Technology, Anhui, Ma’anshan,243032, China
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2024年 / 52卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Combinatorial optimization - Graph theory - Mixers (machinery) - Quantum computers - Quantum optics - Signal encoding;
D O I：
10.12263/DZXB.20220784
中图分类号：
学科分类号：
摘要：
Quantum approximate optimization algorithm (QAOA) is an algorithm framework for solving combinatorial optimization problems. It is regarded as one of the promising candidates to demonstrate the advantages of quantum computing in the near future. Within the QAOA framework, the symmetries of quantum states induced by the binary encoding scheme restrain the performance of QAOA. Inspired by the Dicke state preparation algorithm, we proposed a new encoding scheme that eliminated the symmetry of quantum states representing solutions. Beyond that, we also proposed a novel evolution operator, star graph (SG) mixer, and its corresponding SG algorithm. The quantum circuit implementation of the SG algorithm on IBM Q showed the SG algorithm has an average performance improvement of about 25.3% over the standard QAOA algorithm in solving the graph partitioning problem. © 2024 Chinese Institute of Electronics. All rights reserved.
引用
收藏
页码：2025 / 2036
相关论文
共 50 条
  • [21] Reachability Deficits in Quantum Approximate Optimization of Graph Problems
    Akshay, V
    Philathong, H.
    Zacharov, I
    Biamonte, J. D.
    QUANTUM, 2021, 5
  • [22] Energy landscapes for the quantum approximate optimization algorithm
    Boy, Choy
    Wales, David J.
    PHYSICAL REVIEW A, 2024, 109 (06)
  • [23] Classical symmetries and the Quantum Approximate Optimization Algorithm
    Ruslan Shaydulin
    Stuart Hadfield
    Tad Hogg
    Ilya Safro
    Quantum Information Processing, 2021, 20
  • [24] Classical symmetries and the Quantum Approximate Optimization Algorithm
    Shaydulin, Ruslan
    Hadfield, Stuart
    Hogg, Tad
    Safro, Ilya
    QUANTUM INFORMATION PROCESSING, 2021, 20 (11)
  • [25] Entanglement perspective on the quantum approximate optimization algorithm
    Dupont, Maxime
    Didier, Nicolas
    Hodson, Mark J.
    Moore, Joel E.
    Reagor, Matthew J.
    PHYSICAL REVIEW A, 2022, 106 (02)
  • [26] Quantum approximate optimization algorithm for qudit systems
    Deller, Yannick
    Schmitt, Sebastian
    Lewenstein, Maciej
    Lenk, Steve
    Federer, Marika
    Jendrzejewski, Fred
    Hauke, Philipp
    Kasper, Valentin
    PHYSICAL REVIEW A, 2023, 107 (06)
  • [27] Channel Decoding with Quantum Approximate Optimization Algorithm
    Matsumine, Toshiki
    Koike-Akino, Toshiaki
    Wang, Ye
    2019 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY (ISIT), 2019, : 2574 - 2578
  • [28] Quantum dropout: On and over the hardness of quantum approximate optimization algorithm
    Wang, Zhenduo
    Zheng, Pei-Lin
    Wu, Biao
    Zhang, Yi
    PHYSICAL REVIEW RESEARCH, 2023, 5 (02):
  • [29] Fast approximate graph partitioning algorithms
    Even, G
    Naor, J
    Rao, S
    Schieber, B
    PROCEEDINGS OF THE EIGHTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, 1997, : 639 - 648
  • [30] Fast approximate graph partitioning algorithms
    Even, G
    Naor, JS
    Rao, S
    Schieber, B
    SIAM JOURNAL ON COMPUTING, 1999, 28 (06) : 2187 - 2214
12345