Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimal quantum kernels for small data classification   While quantum machine learning (ML) has been proposed to be one of the most
promising applications of quantum computing, how to build quantum ML models
that outperform classical ML remains a major open question. Here, we
demonstrate an algorithm for constructing quantum kernels for support vector
machines that adapts quantum gate sequences to data. The algorithm includes
three essential ingredients: greedy search in the space of quantum circuits,
Bayesian information criterion as circuit selection metric and Bayesian
optimization of the parameters of the optimal quantum circuit identified. The
performance of the resulting quantum models for classification problems with a
small number of training points significantly exceeds that of optimized
classical models with conventional kernels. In addition, we illustrate the
possibility of mapping quantum circuits onto molecular fingerprints and show
that performant quantum kernels can be isolated in the resulting chemical
space. This suggests that methods developed for optimization and interpolation
of molecular properties across chemical spaces can be used for building quantum
circuits for quantum machine learning with enhanced performance.

27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat