Comprehensive Library of Variational LSE Solvers
Abstract
Linear systems of equations can be found in various mathematical domains, as well as in the field of machine learning. By employing noisy intermediate-scale quantum devices, variational solvers promise to accelerate finding solutions for large systems. Although there is a wealth of theoretical research on these algorithms, only fragmentary implementations exist. To fill this gap, we have developed the variational-lse-solver framework, which realizes existing approaches in literature, and introduces several enhancements. The user-friendly interface is designed for researchers that work at the abstraction level of identifying and developing end-to-end applications.
Cite
@article{arxiv.2404.09916,
title = {Comprehensive Library of Variational LSE Solvers},
author = {Nico Meyer and Martin Röhn and Jakob Murauer and Axel Plinge and Christopher Mutschler and Daniel D. Scherer},
journal= {arXiv preprint arXiv:2404.09916},
year = {2025}
}
Comments
Accepted to the 2nd International Workshop on Quantum Machine Learning: From Research to Practice (QML@QCE 2024), Montr\'eal, Qu\'ebec, Canada. 4 pages, 2 figures, 1 table