Tensorized Pauli decomposition algorithm
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917783448584192 |
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| author | Hantzko, Lukas Binkowski, Lennart Gupta, Sabhyata |
| author_facet | Hantzko, Lukas Binkowski, Lennart Gupta, Sabhyata |
| contents | This paper introduces a novel general-purpose algorithm for Pauli decomposition that employs matrix slicing and addition rather than expensive matrix multiplication, significantly accelerating the decomposition of multi-qubit matrices. In a detailed complexity analysis, we show that the algorithm admits the best known worst-case scaling and more favorable runtimes for many practical examples. Numerical experiments are provided to validate the asymptotic speed-up already for small instance sizes, underscoring the algorithm's potential significance in the realm of quantum computing and quantum chemistry simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_13421 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Tensorized Pauli decomposition algorithm Hantzko, Lukas Binkowski, Lennart Gupta, Sabhyata Quantum Physics This paper introduces a novel general-purpose algorithm for Pauli decomposition that employs matrix slicing and addition rather than expensive matrix multiplication, significantly accelerating the decomposition of multi-qubit matrices. In a detailed complexity analysis, we show that the algorithm admits the best known worst-case scaling and more favorable runtimes for many practical examples. Numerical experiments are provided to validate the asymptotic speed-up already for small instance sizes, underscoring the algorithm's potential significance in the realm of quantum computing and quantum chemistry simulations. |
| title | Tensorized Pauli decomposition algorithm |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2310.13421 |