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Quantum Algorithms for Tensor-SVD

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dc.contributor.author JOJO, JEZER
dc.contributor.author Khandelwal, Ankit
dc.contributor.author Chandra, M. Girish
dc.date.accessioned 2025-04-19T05:42:09Z
dc.date.available 2025-04-19T05:42:09Z
dc.date.issued 2024-09
dc.identifier.citation 2024 IEEE International Conference on Quantum Computing and Engineering (QCE) en_US
dc.identifier.isbn 979-8-3315-4137-8
dc.identifier.isbn 979-8-3315-4138-5
dc.identifier.uri https://doi.org/10.1109/QCE60285.2024.00018 en_US
dc.identifier.uri http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/9650
dc.description.abstract A 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.iso en en_US
dc.publisher IEEE en_US
dc.subject Machine learning algorithms en_US
dc.subject Quantum algorithm en_US
dc.subject Linear algebra en_US
dc.subject Recommender systems en_US
dc.subject 2024 en_US
dc.title Quantum Algorithms for Tensor-SVD en_US
dc.type Conference Papers en_US
dc.contributor.department Dept. of Physics en_US
dc.identifier.doi https://doi.org/10.1109/QCE60285.2024.00018 en_US
dc.identifier.sourcetitle 2024 IEEE International Conference on Quantum Computing and Engineering (QCE) en_US
dc.publication.originofpublisher Foreign en_US


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