A Study on the Leapfrogging Strategy and Parameters Fixing for the Quantum Approximate Optimization Algorithm on the Max-cut of <i>n</i>-regular Graph Instances

• Item type: SIG Technical Reports(1)
• 公開日: 2021-03-22
• タイトル: A Study on the Leapfrogging Strategy and Parameters Fixing for the Quantum Approximate Optimization Algorithm on the Max-cut of <i>n</i>-regular Graph Instances
• タイトル / 言語: en
• タイトル: A Study on the Leapfrogging Strategy and Parameters Fixing for the Quantum Approximate Optimization Algorithm on the Max-cut of <i>n</i>-regular Graph Instances
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: University of Tsukuba
• 著者所属: University of Tsukuba
• 著者所属(英): en
• 著者所属(英): University of Tsukuba
• 著者所属(英): en
• 著者所属(英): University of Tsukuba
• 著者名: Xinwei, Lee; Dongsheng, Cai
• 著者名(英): Xinwei, Lee; Dongsheng, Cai
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: The quantum approximate optimization algorithm (QAOA) has numerous promising applications on solving the combinatorial optimization problems on the near-term Noisy Intermediate Scalable Quantum (NISQ) devices. QAOA has a quantum-classical hybrid structure, with the quantum part consisting the parameterized alternating operator ansatz, and the classical part consist of an optimization algorithm optimizing the parameters to maximize the expectation value. This value depends highly on the parameters. This implies that a set of good parameters leads to an accurate solution of the given problem. However, at large circuit depth, it is difficult to achieve global optimization due to the multiple occurrence of local maxima. Therefore, we study the so-called leapfrogging strategy on solving the Max-cut problem for 3-regular graphs, which reuses the optimized parameters in larger graphs. Also, we propose a strategy of parameters fixing to increase the quality of the results as the circuit depth gets larger.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: The quantum approximate optimization algorithm (QAOA) has numerous promising applications on solving the combinatorial optimization problems on the near-term Noisy Intermediate Scalable Quantum (NISQ) devices. QAOA has a quantum-classical hybrid structure, with the quantum part consisting the parameterized alternating operator ansatz, and the classical part consist of an optimization algorithm optimizing the parameters to maximize the expectation value. This value depends highly on the parameters. This implies that a set of good parameters leads to an accurate solution of the given problem. However, at large circuit depth, it is difficult to achieve global optimization due to the multiple occurrence of local maxima. Therefore, we study the so-called leapfrogging strategy on solving the Max-cut problem for 3-regular graphs, which reuses the optimized parameters in larger graphs. Also, we propose a strategy of parameters fixing to increase the quality of the results as the circuit depth gets larger.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12894105
• 書誌情報: 研究報告量子ソフトウェア（QS）; 巻 2021-QS-2, 号 12, p. 1-6, 発行日 2021-03-22
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2435-6492
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会