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dc.contributor.authorJOJO, JEZER-
dc.contributor.authorKhandelwal, Ankit-
dc.contributor.authorChandra, M. Girish-
dc.date.accessioned2025-04-19T05:42:09Z-
dc.date.available2025-04-19T05:42:09Z-
dc.date.issued2024-09-
dc.identifier.citation2024 IEEE International Conference on Quantum Computing and Engineering (QCE)en_US
dc.identifier.isbn979-8-3315-4137-8-
dc.identifier.isbn979-8-3315-4138-5-
dc.identifier.urihttps://doi.org/10.1109/QCE60285.2024.00018en_US
dc.identifier.urihttp://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/9650-
dc.description.abstractA promising area of applications for quantum computing is in linear algebra problems. In this work, we introduce two new quantum t-SVD (tensor-SVD) algorithms. The first algorithm is largely based on previous work that proposed a quantum t-SVD algorithm for context-aware recommendation systems. The new algorithm however seeks to address and fix certain drawbacks in the original, and is fundamentally different in its approach compared to the existing work. The second algorithm proposed uses a hybrid variational approach largely based on a known variational quantum SVD algorithm.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectMachine learning algorithmsen_US
dc.subjectQuantum algorithmen_US
dc.subjectLinear algebraen_US
dc.subjectRecommender systemsen_US
dc.subject2024en_US
dc.titleQuantum Algorithms for Tensor-SVDen_US
dc.typeConference Papersen_US
dc.contributor.departmentDept. of Physicsen_US
dc.identifier.doihttps://doi.org/10.1109/QCE60285.2024.00018en_US
dc.identifier.sourcetitle2024 IEEE International Conference on Quantum Computing and Engineering (QCE)en_US
dc.publication.originofpublisherForeignen_US
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