  Current quantum systems have significant limitations affecting the processing
of large datasets with high dimensionality, typical of high energy physics. In
the present paper, feature and data prototype selection techniques were studied
to tackle this challenge. A grid search was performed and quantum machine
learning models were trained and benchmarked against classical shallow machine
learning methods, trained both in the reduced and the complete datasets. The
performance of the quantum algorithms was found to be comparable to the
classical ones, even when using large datasets. Sequential Backward Selection
and Principal Component Analysis techniques were used for feature's selection
and while the former can produce the better quantum machine learning models in
specific cases, it is more unstable. Additionally, we show that such
variability in the results is caused by the use of discrete variables,
highlighting the suitability of Principal Component analysis transformed data
for quantum machine learning applications in the high energy physics context.

High Energy Physics - Phenomenology;High Energy Physics - Experiment;Data Analysis, Statistics and Probability;Quantum Physics