This paper introduces a novel quantum embedding search algorithm (QES, pronounced as "quest"), enabling search for optimal quantum embedding design for a specific dataset of interest. First, we establish the connection between the structures of quantum embedding and the representations of directed multi-graphs, enabling a well-defined search space. Second, we instigate the entanglement level to reduce the cardinality of the search space to a feasible size for practical implementations. Finally, we mitigate the cost of evaluating the true loss function by using surrogate models via sequential model-based optimization. We demonstrate the feasibility of our proposed approach on synthesis and Iris datasets, which empirically shows that found quantum embedding architecture by QES outperforms manual designs whereas achieving comparable performance to classical machine learning models.

翻译：本文介绍一种新的量子嵌入搜索算法(QES, 以“ 遗赠 ” 命名), 从而能够搜索用于特定关注数据集的最佳量子嵌入设计。 首先, 我们建立量子嵌入结构与定向多面图的表达方式之间的联系, 从而可以有一个定义明确的搜索空间。 第二, 我们鼓励纠缠层, 将搜索空间的基点缩小到可行的大小, 以便实际执行。 最后, 我们通过按顺序使用模型优化的代用模型来降低评估真正损失功能的成本。 我们展示了我们所提议的合成和Iris数据集方法的可行性, 其实验性地显示, QES 的量子嵌入结构比人工设计更符合质量要求, 而能取得与经典机器学习模型的可比性能 。