Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quantum evolution kernel : Machine learning on graphs with programmable
  arrays of qubits   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.

34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
1
The categories of this paper are: cs