Please use this identifier to cite or link to this item: http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/9650
Title: Quantum Algorithms for Tensor-SVD
Authors: JOJO, JEZER
Khandelwal, Ankit
Chandra, M. Girish
Dept. of Physics
Keywords: Machine learning algorithms
Quantum algorithm
Linear algebra
Recommender systems
2024
Issue Date: Sep-2024
Publisher: IEEE
Citation: 2024 IEEE International Conference on Quantum Computing and Engineering (QCE)
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.
URI: https://doi.org/10.1109/QCE60285.2024.00018
http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/9650
ISBN: 979-8-3315-4137-8
979-8-3315-4138-5
Appears in Collections:CONFERENCE PAPERS

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