Fitting a collider in a quantum computer: tackling the challenges of quantum machine learning for big datasets

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frai.2023.1268852/full#supplementary-materialCódigo computacional disponível em: https://github.com/mcpeixoto/QML-HEPCurrent 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.The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by Fundação para a Ciência e a Tecnologia, Portugal, through project CERN/FIS-COM/0004/2021 (“Exploring quantum machine learning as a tool for present and future high energy physics colliders”). IO was supported by the fellowship LCF/BQ/PI20/11760025 from La Caixa Foundation (ID 100010434) and by the European Union Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 847648.