Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Event Classification with Quantum Machine Learning in High-Energy
  Physics   We present studies of quantum algorithms exploiting machine learning to
classify events of interest from background events, one of the most
representative machine learning applications in high-energy physics. We focus
on variational quantum approach to learn the properties of input data and
evaluate the performance of the event classification using both simulators and
quantum computing devices. Comparison of the performance with standard
multi-variate classification techniques based on a boosted-decision tree and a
deep neural network using classical computers shows that the quantum algorithm
has comparable performance with the standard techniques at the considered
ranges of the number of input variables and the size of training samples. The
variational quantum algorithm is tested with quantum computers, demonstrating
that the discrimination of interesting events from background is feasible.
Characteristic behaviors observed during a learning process using quantum
circuits with extended gate structures are discussed, as well as the
implications of the current performance to the application in high-energy
physics experiments.

37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat