Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quantum Multiple Kernel Learning   Kernel methods play an important role in machine learning applications due to
their conceptual simplicity and superior performance on numerous machine
learning tasks. Expressivity of a machine learning model, referring to the
ability of the model to approximate complex functions, has a significant
influence on its performance in these tasks. One approach to enhancing the
expressivity of kernel machines is to combine multiple individual kernels to
arrive at a more expressive combined kernel. This approach is referred to as
multiple kernel learning (MKL). In this work, we propose an MKL method we refer
to as quantum MKL, which combines multiple quantum kernels. Our method
leverages the power of deterministic quantum computing with one qubit (DQC1) to
estimate the combined kernel for a set of classically intractable individual
quantum kernels. The combined kernel estimation is achieved without explicitly
computing each individual kernel, while still allowing for the tuning of
individual kernels in order to achieve better expressivity. Our simulations on
two binary classification problems---one performed on a synthetic dataset and
the other on a German credit dataset---demonstrate the superiority of the
quantum MKL method over single quantum kernel machines.

18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math