标题： 研究QAOA参数在Max-Cut问题中的层选择性迁移学习 显示英文标题

标题： Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem

摘要： 量子近似优化算法（QAOA）是一种适用于噪声中等规模量子（NISQ）处理器的变分量子算法（VQA），在解决组合优化问题（COPs）方面非常成功。 已经观察到，从一个COP实例中获得的最优变分参数可以转移到另一个实例，为后者产生足够令人满意的解决方案。 在此背景下，进一步提高解决方案的合适方法是微调部分转移的参数。 我们数值地探讨了在参数转移后优化单个QAOA层在改进Max-Cut问题近似解中的作用。 我们还研究了在优化转移后的QAOA参数时，良好近似与所需优化时间之间的权衡。 这些研究显示，与优化所有层相比，优化部分层可以在更低的时间成本下更有效地提升结果。 显示英文摘要

摘要： Quantum approximate optimization algorithm (QAOA) is a variational quantum algorithm (VQA) ideal for noisy intermediate-scale quantum (NISQ) processors, and is highly successful for solving combinatorial optimization problems (COPs). It has been observed that the optimal variational parameters obtained from one instance of a COP can be transferred to another instance, producing sufficiently satisfactory solutions for the latter. In this context, a suitable method for further improving the solution is to fine-tune a subset of the transferred parameters. We numerically explore the role of optimizing individual QAOA layers in improving the approximate solution of the Max-Cut problem after parameter transfer. We also investigate the trade-off between a good approximation and the required optimization time when optimizing transferred QAOA parameters. These studies show that optimizing a subset of layers can be more effective at a lower time-cost compared to optimizing all layers.