The rapid development of reliable Quantum Processing Units (QPU) opens up
novel computational opportunities for machine learning. Here, we introduce a
procedure for measuring the similarity between graph-structured data, based on
the time-evolution of a quantum system. By encoding the topology of the input
graph in the Hamiltonian of the system, the evolution produces measurement
samples that retain key features of the data. We study analytically the
procedure and illustrate its versatility in providing links to standard
classical approaches. We then show numerically that this scheme performs well
compared to standard graph kernels on typical benchmark datasets. Finally, we
study the possibility of a concrete implementation on a realistic neutral-atom
quantum processor.

本文介绍了一种基于量子系统时间演化的图形数据相似性测量程序，并探讨了其在传统方法中的应用。通过在系统哈密顿量中编码输入图形的拓扑结构，演化产生保留数据关键特征的测量样本。数值研究表明，与典型基准数据集上的标准图形核相比，该程序表现出色。最后，讨论了在真实的中性原子量子处理器上实现的可能性。