Comprehensive Library of Variational LSE Solvers
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 5 of its 19 references that are in the map. First posted on arXiv 15 Apr 2024.
Builds on
5 of 19 references.
References · 5
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2020Variational Quantum Algorithmscited by 692
- 2022A Survey on Quantum Reinforcement Learningcited by 53
- 2023Towards provably efficient quantum algorithms for large-scale machine-learning modelscited by 32
- 2022Quantum Reinforcement Learning via Policy Iterationcited by 19
Built on by
Citing papers per year: 2024: 2.
2 papers.