000133390 001__ 133390
000133390 005__ 20250923084424.0
000133390 0247_ $$2doi$$a10.1088/2632-2153/ad360d
000133390 0248_ $$2sideral$$a138089
000133390 037__ $$aART-2024-138089
000133390 041__ $$aeng
000133390 100__ $$aRoca-Jerat, Sebastián
000133390 245__ $$aQudit machine learning
000133390 260__ $$c2024
000133390 5060_ $$aAccess copy available to the general public$$fUnrestricted
000133390 5203_ $$aWe 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.
000133390 536__ $$9info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131447B-C21$$9info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2020-115221GB-C41$$9info:eu-repo/grantAgreement/ES/MCIU/FPU20-07231$$9info:eu-repo/grantAgreement/ES/DGA/E09-17R-Q-MAD
000133390 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000133390 590__ $$a4.6$$b2024
000133390 592__ $$a1.119$$b2024
000133390 591__ $$aMULTIDISCIPLINARY SCIENCES$$b20 / 135 = 0.148$$c2024$$dQ1$$eT1
000133390 593__ $$aArtificial Intelligence$$c2024$$dQ1
000133390 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b60 / 204 = 0.294$$c2024$$dQ2$$eT1
000133390 593__ $$aSoftware$$c2024$$dQ1
000133390 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b50 / 175 = 0.286$$c2024$$dQ2$$eT1
000133390 593__ $$aHuman-Computer Interaction$$c2024$$dQ1
000133390 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000133390 700__ $$0(orcid)0000-0003-2995-6615$$aRomán-Roche, Juan
000133390 700__ $$0(orcid)0000-0003-4478-1948$$aZueco, David
000133390 773__ $$g5, 1 (2024), 015057$$tMachine Learning: Science and Technology$$x2632-2153
000133390 8564_ $$s2107783$$uhttps://zaguan.unizar.es/record/133390/files/texto_completo.pdf$$yVersión publicada
000133390 8564_ $$s2729175$$uhttps://zaguan.unizar.es/record/133390/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000133390 909CO $$ooai:zaguan.unizar.es:133390$$particulos$$pdriver
000133390 951__ $$a2025-09-22-14:38:19
000133390 980__ $$aARTICLE