This article explores search strategies for the design of parameterized quantum circuits. We propose several optimization approaches including random search plus survival of the fittest, reinforcement learning both with classical and hybrid quantum classical controllers and Bayesian optimization as decision makers to design a quantum circuit in an automated way for a specific task such as multi-labeled classification over a dataset. We introduce nontrivial circuit architectures that are arduous to be hand-designed and efficient in terms of trainability. In addition, we introduce reuploading of initial data into quantum circuits as an option to find more general designs. We numerically show that some of the suggested architectures for the Iris dataset accomplish better results compared to the established parameterized quantum circuit designs in the literature. In addition, we investigate the trainability of these structures on the unseen dataset Glass. We report meaningful advantages over the benchmarks for the classification of the Glass dataset which supports the fact that the suggested designs are inherently more trainable.

翻译：文章探索参数化量子电路设计的搜索策略 。 我们提出几种优化方法, 包括随机搜索和适者生存, 用古典和混合量子古典控制器加强学习, 以及作为决策者的巴伊西亚优化, 以自动的方式设计量子电路, 以完成数据集上多标签分类等特定任务。 我们引入了非三重电路结构, 这些非三重电路结构在可训练性方面很难手工设计和高效。 此外, 我们引入了将初始数据重新装入量子电路, 作为寻找更通用设计的选项 。 我们用数字显示, Iris 数据集的一些建议结构取得了比文献中既定参数化量子电路设计更好的结果 。 此外, 我们还调查了这些结构在无形数据集中的可训练性 。 我们报告了玻璃数据集分类基准的显著优势, 这证明所建议的设计本质上更易培训 。