Qudit machine learning

Roca-Jerat, Sebastián ; Román-Roche, Juan ; Zueco, David

Resumen: We present a comprehensive investigation into the learning capabilities of a simple d-level system (qudit). Our study is specialized for classification tasks using real-world databases, specifically the Iris, breast cancer, and MNIST datasets. We explore various learning models in the metric learning framework, along with different encoding strategies. In particular, we employ data re-uploading techniques and maximally orthogonal states to accommodate input data within low-dimensional systems. Our findings reveal optimal strategies, indicating that when the dimension of input feature data and the number of classes are not significantly larger than the qudit’s dimension, our results show favorable comparisons against the best classical models. This trend holds true even for small quantum systems, with dimensions d<5 and utilizing algorithms with a few layers (L =1,2). However, for high-dimensional data such as MNIST, we adopt a hybrid approach involving dimensional reduction through a convolutional neural network. In this context, we observe that small quantum systems often act as bottlenecks, resulting in lower accuracy compared to their classical counterparts.

Idioma: Inglés

DOI: 10.1088/2632-2153/ad360d

Año: 2024

Publicado en: Machine Learning: Science and Technology 5, 1 (2024), 015057
ISSN: 2632-2153

Factor impacto JCR: 4.6 (2024)

Categ. JCR: MULTIDISCIPLINARY SCIENCES rank: 20 / 135 = 0.148 (2024) - Q1 - T1
Categ. JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE rank: 60 / 204 = 0.294 (2024) - Q2 - T1
Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 50 / 175 = 0.286 (2024) - Q2 - T1

Factor impacto SCIMAGO: 1.119 - Artificial Intelligence (Q1) - Software (Q1) - Human-Computer Interaction (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/E09-17R-Q-MAD
Financiación: info:eu-repo/grantAgreement/ES/MCIU/FPU20-07231
Financiación: info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2020-115221GB-C41
Financiación: info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131447B-C21

Tipo y forma: Article (Published version)