Comprehensive Library of Variational LSE Solvers
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.
Authors
- Axel Plinge (ORCID: https://orcid.org/0000-0001-7757-2953)
- Christopher Mutschler (ORCID: https://orcid.org/0000-0001-8108-0230)
- Daniel D. Scherer (ORCID: https://orcid.org/0000-0003-0355-4140)
- Nico Meyer (ORCID: https://orcid.org/0000-0002-5463-5437)
- Martin Röhn
- Jakob Murauer
Institutions
- Fraunhofer Institute for Integrated Circuits (DE)
Publication Details
- Published
- 2024-09-15
- DOI
- https://doi.org/10.1109/qce60285.2024.10242
- Citations
- 1
- Primary Topic
- Numerical methods for differential equations
- Type
- article
- Field-Weighted Citation Impact
- 0.73