The Quantum Approximate Optimization Algorithm (QAOA) and its advanced variant, the Quantum Alternating Operator Ansatz (QAOA), are major research topics in the current era of Noisy Intermediate-Scale Quantum (NISQ) computing. However, the problem of initializing their parameters remains unresolved. Motivated by the combinatorial optimization task in the 6th MindSpore Quantum Computing Hackathon (2024), this paper proposes Stone-in-Waiting, a cloud-based accelerator for obtaining high-quality initial parameters for QAOA. Internally, the accelerator builds on state-of-the-art theories and methods for parameter determination and integrates four self-developed algorithms for QAOA parameter initialization, mainly based on Bayesian methods, nearest-neighbor methods, and metric learning. Compared with the Baseline Algorithm, the generated parameters improve the score by 40.19%. Externally, the accelerator offers both a web interface and an API, providing flexible and convenient access for users to test and develop related experiments and applications. This paper presents the design principles and methods of Stone-in-Waiting, demonstrates its functional characteristics, compares the strengths and weaknesses of the four proposed algorithms, and validates the overall system performance through experiments.

翻译：量子近似优化算法（QAOA）及其改进版本——量子交替算子拟设（QAOA）——是当前含噪中等规模量子（NISQ）计算时代的主要研究课题。然而，其参数初始化问题仍未得到解决。受第六届MindSpore量子计算黑客松（2024）中组合优化任务的启发，本文提出了一种名为"待石"的基于云加速器，用于获取QAOA的高质量初始参数。该加速器内部基于参数确定的最新理论和方法，集成了四种自研的QAOA参数初始化算法，主要基于贝叶斯方法、最近邻方法和度量学习。与基线算法相比，生成的参数将得分提升了40.19%。在外部，该加速器提供网页界面和API接口，为用户测试和开发相关实验与应用提供灵活便捷的访问方式。本文阐述了"待石"的设计原理与方法，展示了其功能特性，比较了四种算法的优劣，并通过实验验证了系统整体性能。