Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/87943
Título: | Fitting a collider in a quantum computer: tackling the challenges of quantum machine learning for big datasets |
Autor(es): | Peixoto, Miguel Caçador Castro, Nuno Filipe Romão, Miguel Crispim Oliveira, Maria Gabriela Jordão Ochoa, Inês |
Palavras-chave: | computação quântica física de partículas data reduction high energy physics K-means principal component analysis quantum computing quantum machine learning |
Data: | Dez-2023 |
Editora: | Frontiers Media |
Revista: | Frontiers in Artificial Intelligence and Applications |
Citação: | Peixoto MC, Castro NF, Crispim Romão M, Oliveira MGJ and Ochoa I (2023) Fitting a collider in a quantum computer: tackling the challenges of quantum machine learning for big datasets. Front. Artif. Intell. 6:1268852. doi: 10.3389/frai.2023.1268852 |
Resumo(s): | 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. |
Tipo: | Artigo |
Descrição: | The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frai.2023.1268852/full#supplementary-material Código computacional disponível em: https://github.com/mcpeixoto/QML-HEP |
URI: | https://hdl.handle.net/1822/87943 |
DOI: | 10.3389/frai.2023.1268852 |
e-ISSN: | 2624-8212 |
Versão da editora: | https://www.frontiersin.org/articles/10.3389/frai.2023.1268852/full |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | LIP - Artigos/papers |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
2211.03233.pdf | 848,63 kB | Adobe PDF | Ver/Abrir | |
frai-06-1268852.pdf | 3,59 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons