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

Fitting a Collider in a Quantum Computer: Tackling the Challenges of Quantum Machine Learning for Big Datasets