On Modifying the Variational Quantum Singular Value Decomposition Algorithm

Abstract

In this work, we discuss two modifications that can be made to a known variational quantum singular value decomposition algorithm popular in the literature. The first is a change to the objective function which hints at improved performance of the algorithm. The second modification introduces a new way of computing expectation values of general matrices, which is a key step in the algorithm. We then benchmark this modified algorithm and compare the performance of our new objective function with the existing one.

8 pages, 10 figures, 2 tables

References in corpus (5)

• Variational Quantum Singular Value Decomposition
• Block-encoding structured matrices for data input in quantum computing
• Quantum pixel representations and compression for $N$-dimensional images
• FABLE: Fast Approximate Quantum Circuits for Block-Encodings
• Quantum algorithms for SVD-based data representation and analysis

Cited by in corpus (1)

• Quantum Algorithms for tensor-SVD