Quantum feature maps for graph machine learning on a neutral atom quantum processor

Abstract

Using a Rydberg quantum processor with up to 32 qubits, the authors implement machine learning tasks on data structured into graphs and show that their platform can distinguish two different graph connectivities. To illustrate the potential of such a method, they show that it can classify the toxicity of a given molecule based on a real-world biochemistry dataset.