A Variational Quantum Algorithm for Ordered SVD

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Singular value decomposition (SVD) is fundamentally important and broadly useful in both quantum and classical computing. There are several quantum algorithms known for SVD. Variational quantum algorithms with parametric quantum circuits (PQC) are the most promising approach to find SVD with near-term quantum computers. This paper reports a new method for quantum SVD (QSVD) that finds the singular vectors ordered by the magnitude of singular values by adding extra CNOT gates and a new local cost function design. This ordering is important because the singular vectors with the larger singular values is more representative for the given information. The ordering property of this approach is investigated with the standard Iris dataset by numerical simulations of 4-qubit states. Methods for choosing appropriate choices for the cost function hyperparameter and cost function terms as well as an application example of a quantum encoder are also discussed.
Publisher
IEEE
Issue Date
2021-12
Language
English
Citation

GLOBECOM 2021 - 2021 IEEE Global Communications Conference

ISSN
2334-0983
DOI
10.1109/globecom46510.2021.9685404
URI
http://hdl.handle.net/10203/312251
Appears in Collection
EE-Conference Papers(학술회의논문)
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