Unlocking the high dimensional’ potential: Comparative analysis of qubits and qutrits in variational quantum neural networks

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dc.authoridhttps://orcid.org/0000-0002-1451-7874
dc.contributor.authorAcar, Erdi
dc.contributor.authorYılmaz, İhsan
dc.date.accessioned2025-06-23T06:52:47Z
dc.date.available2025-06-23T06:52:47Z
dc.date.issued2025
dc.departmentMühendislik ve Mimarlık Fakültesi
dc.description.abstractQuantum machine learning is a promising research area with great potential. In particular, Variational quantum neural networks (VQNN) have shown high performance in many applications. However, while qubits, which are 2-level quantum systems, are the standard building blocks of quantum computing, the development of qudits, i.e. d-level quantum systems, has opened up new opportunities in VQNNs thanks to many properties such as robustness to noise and more quantum information processing with fewer quantum resources. In this study, we present a comparative analysis of qubits and qutrits (3-level quantum systems) systems performance in VQNNs while also exploring the effect of encoding strategies and entanglement on classifier performance. Our findings contribute to a better understanding the benefits and limitations of using qutrits in VQNN and pave the way for future developments in this field.
dc.identifier.doi10.1016/j.neucom.2025.129404
dc.identifier.issn0925-2312
dc.identifier.issn1872-8286
dc.identifier.urihttps://hdl.handle.net/11363/9971
dc.identifier.volume623
dc.identifier.wos001414172900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.institutionauthorAcar, Erdi
dc.institutionauthoridhttps://orcid.org/0000-0002-1451-7874
dc.language.isoen
dc.publisherELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
dc.relation.ispartofNEUROCOMPUTING
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectQubit
dc.subjectQutrit
dc.subjectVariational quantum circuit
dc.subjectVariational quantum neural networks
dc.titleUnlocking the high dimensional’ potential: Comparative analysis of qubits and qutrits in variational quantum neural networks
dc.typeArticle

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