2021/08/11 by Xinwei Lee, Yoshiyuki Saito, Dongsheng Cai +1 · 4 citations
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Algorithm #Computer science #Discrete mathematics #Graph #Hamiltonian (control theory) #Initialization #Low-power high-performance VLSI design #Mathematical optimization #Mathematics #Maxima and minima #Maximum cut #Operator (biology) #Optimization problem #Parameterized complexity #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum algorithm #Quantum mechanics #Scalability #quant-ph
paper · pdf · doi:10.1109/qce52317.2021.00016
7 pages, 5 figures, accepted in the IEEE International Conference on Quantum Computing and Engineering
arxiv created 2021/08/11 · openalex publication_date 2021/10/01 · arxiv updated 2022/02/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
The quantum approximate optimization algorithm (QAOA) has numerous promising applications in solving the combinatorial optimization problems on near-term Noisy Intermediate Scalable Quantum (NISQ) devices. QAOA has a quantum-classical hybrid structure. Its quantum part consists of a parameterized alternating operator ansatz, and its classical part comprises an optimization algorithm, which optimizes the parameters to maximize the expectation value of the problem Hamiltonian. This expectation value depends highly on the parameters, this implies that a set of good parameters leads to an accurate solution. However, at large circuit depth of QAOA, it is difficult to achieve global optimization due to the multiple occurrences of local minima or maxima. In this paper, we propose a parameters fixing strategy which gives high approximation ratio on average, even at large circuit depths, by initializing QAOA with the optimal parameters obtained from the previous depths. We test our strategy on the Max-cut problem of certain classes of graphs such as the 3-regular graphs and the Erdös-Rényi graphs.