  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.
