Comprehensive Library of Variational LSE Solvers
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866912187851735040 |
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| author | Meyer, Nico Röhn, Martin Murauer, Jakob Plinge, Axel Mutschler, Christopher Scherer, Daniel D. |
| author_facet | Meyer, Nico Röhn, Martin Murauer, Jakob Plinge, Axel Mutschler, Christopher Scherer, Daniel D. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09916 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Comprehensive Library of Variational LSE Solvers Meyer, Nico Röhn, Martin Murauer, Jakob Plinge, Axel Mutschler, Christopher Scherer, Daniel D. Quantum Physics Machine Learning Software Engineering 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. |
| title | Comprehensive Library of Variational LSE Solvers |
| topic | Quantum Physics Machine Learning Software Engineering |
| url | https://arxiv.org/abs/2404.09916 |