  Using a quantum processor to embed and process classical data enables the
generation of correlations between variables that are inefficient to represent
through classical computation. A fundamental question is whether these
correlations could be harnessed to enhance learning performances on real
datasets. Here, we report the use of a neutral atom quantum processor
comprising up to $32$ qubits to implement machine learning tasks on
graph-structured data. To that end, we introduce a quantum feature map to
encode the information about graphs in the parameters of a tunable Hamiltonian
acting on an array of qubits. Using this tool, we first show that interactions
in the quantum system can be used to distinguish non-isomorphic graphs that are
locally equivalent. We then realize a toxicity screening experiment, consisting
of a binary classification protocol on a biochemistry dataset comprising $286$
molecules of sizes ranging from $2$ to $32$ nodes, and obtain results which are
comparable to those using the best classical kernels. Using techniques to
compare the geometry of the feature spaces associated with kernel methods, we
then show evidence that the quantum feature map perceives data in an original
way, which is hard to replicate using classical kernels.
