On Modifying the Variational Quantum Singular Value Decomposition Algorithm
Jezer Jojo, Ankit Khandelwal, M Girish Chandra

TL;DR
This paper proposes two modifications to a variational quantum SVD algorithm, including an improved objective function and a new method for computing expectation values, leading to enhanced performance.
Contribution
It introduces a novel objective function and a new expectation value computation method for the variational quantum SVD algorithm, with benchmarking results.
Findings
Modified objective function shows improved algorithm performance
New expectation value method enhances computational efficiency
Benchmarking confirms better accuracy and speed
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.
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography
