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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2412.18973 |
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| _version_ | 1866909441212809216 |
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| author | Van Kirk, Katherine Kokail, Christian Kunjummen, Jonathan Hu, Hong-Ye Teng, Yanting Cain, Madelyn Taylor, Jacob Yelin, Susanne F. Pichler, Hannes Lukin, Mikhail |
| author_facet | Van Kirk, Katherine Kokail, Christian Kunjummen, Jonathan Hu, Hong-Ye Teng, Yanting Cain, Madelyn Taylor, Jacob Yelin, Susanne F. Pichler, Hannes Lukin, Mikhail |
| contents | Efficiently estimating large numbers of non-commuting observables is an important subroutine of many quantum science tasks. We present the derandomized shallow shadows (DSS) algorithm for efficiently learning a large set of non-commuting observables, using shallow circuits to rotate into measurement bases. Exploiting tensor network techniques to ensure polynomial scaling of classical resources, our algorithm outputs a set of shallow measurement circuits that approximately minimizes the sample complexity of estimating a given set of Pauli strings. We numerically demonstrate systematic improvement, in comparison with state-of-the-art techniques, for energy estimation of quantum chemistry benchmarks and verification of quantum many-body systems, and we observe DSS's performance consistently improves as one allows deeper measurement circuits. These results indicate that in addition to being an efficient, low-depth, stand-alone algorithm, DSS can also benefit many larger quantum algorithms requiring estimation of multiple non-commuting observables. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18973 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Derandomized shallow shadows: Efficient Pauli learning with bounded-depth circuits Van Kirk, Katherine Kokail, Christian Kunjummen, Jonathan Hu, Hong-Ye Teng, Yanting Cain, Madelyn Taylor, Jacob Yelin, Susanne F. Pichler, Hannes Lukin, Mikhail Quantum Physics Strongly Correlated Electrons Machine Learning Efficiently estimating large numbers of non-commuting observables is an important subroutine of many quantum science tasks. We present the derandomized shallow shadows (DSS) algorithm for efficiently learning a large set of non-commuting observables, using shallow circuits to rotate into measurement bases. Exploiting tensor network techniques to ensure polynomial scaling of classical resources, our algorithm outputs a set of shallow measurement circuits that approximately minimizes the sample complexity of estimating a given set of Pauli strings. We numerically demonstrate systematic improvement, in comparison with state-of-the-art techniques, for energy estimation of quantum chemistry benchmarks and verification of quantum many-body systems, and we observe DSS's performance consistently improves as one allows deeper measurement circuits. These results indicate that in addition to being an efficient, low-depth, stand-alone algorithm, DSS can also benefit many larger quantum algorithms requiring estimation of multiple non-commuting observables. |
| title | Derandomized shallow shadows: Efficient Pauli learning with bounded-depth circuits |
| topic | Quantum Physics Strongly Correlated Electrons Machine Learning |
| url | https://arxiv.org/abs/2412.18973 |