Higher-Order Methods for Hamiltonian Engineering Pulse Sequence Design
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866916088852250624 |
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| author | Tyler, Matthew Zhou, Hengyun Martin, Leigh S. Leitao, Nathaniel Lukin, Mikhail D. |
| author_facet | Tyler, Matthew Zhou, Hengyun Martin, Leigh S. Leitao, Nathaniel Lukin, Mikhail D. |
| contents | We introduce a framework for designing Hamiltonian engineering pulse sequences that systematically accounts for the effects of higher-order contributions to the Floquet-Magnus expansion. Our techniques result in simple, intuitive decoupling rules, despite the higher-order contributions naively involving complicated, non-local-in-time commutators. We illustrate how these rules can be used to efficiently design improved Hamiltonian engineering pulse sequences for a wide variety of tasks, such as dynamical decoupling, quantum sensing, and quantum simulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_07374 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Higher-Order Methods for Hamiltonian Engineering Pulse Sequence Design Tyler, Matthew Zhou, Hengyun Martin, Leigh S. Leitao, Nathaniel Lukin, Mikhail D. Quantum Physics Disordered Systems and Neural Networks We introduce a framework for designing Hamiltonian engineering pulse sequences that systematically accounts for the effects of higher-order contributions to the Floquet-Magnus expansion. Our techniques result in simple, intuitive decoupling rules, despite the higher-order contributions naively involving complicated, non-local-in-time commutators. We illustrate how these rules can be used to efficiently design improved Hamiltonian engineering pulse sequences for a wide variety of tasks, such as dynamical decoupling, quantum sensing, and quantum simulation. |
| title | Higher-Order Methods for Hamiltonian Engineering Pulse Sequence Design |
| topic | Quantum Physics Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2303.07374 |