Quantum Algorithms for Tensor-SVD

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.

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Publication Details

Published
2024-09-15
DOI
https://doi.org/10.1109/qce60285.2024.00018
Citations
1
Primary Topic
Computational Physics and Python Applications
Type
article
Field-Weighted Citation Impact
0.35
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article

Quantum Algorithms for Tensor-SVD

M Girish Chandra, Ankit Khandelwal, Jezer Jojo
1 citations
Computational Physics and Python Applications
0.35
article

Quantum Algorithms for Tensor-SVD

M Girish Chandra, Ankit Khandelwal, Jezer Jojo
article en
1 citations

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.

Indian Institute of Science Education and Research Pune (IN), Tata Consultancy Services (India) (IN)
Openalex Percentile: Top 30%
Computational Physics and Python Applications
0.35
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