Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.03420 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914160634232832 |
|---|---|
| author | Chen, Jin-Fu Rai, Kshiti Sneh Emonts, Patrick Farina, Donato Płodzień, Marcin Grzybowski, Przemyslaw Lewenstein, Maciej Tura, Jordi |
| author_facet | Chen, Jin-Fu Rai, Kshiti Sneh Emonts, Patrick Farina, Donato Płodzień, Marcin Grzybowski, Przemyslaw Lewenstein, Maciej Tura, Jordi |
| contents | Understanding and optimizing the relaxation dynamics of many-body systems is essential both for foundational studies in quantum thermodynamics and for applications such as quantum simulation and quantum computing. Efficient preparation of thermal states of a many-body Hamiltonian is governed by the spectral properties of the associated Lindbladian, in particular its spectral gap, which determines the slowest relaxation rate. In this work, we develop a systematic framework for constructing Lindbladians that prepare thermal states. Our approach reveals a simple relation between the relaxation dynamics at finite and infinite temperatures. The framework is scalable to larger system sizes when implemented using tensor-network methods. We find that efficient thermalization requires that the relaxation dynamics respect the symmetries of the thermal state, which reduces the number of free parameters. By applying gradient-based optimization to the Lindbladians, we enhance the spectral gap and thereby boost thermalization. When applied to both classical and quantum spin models, our method demonstrates a substantial enhancement of the spectral gap. For larger system sizes, our approach provides a variational upper bound and enables a certified lower bound on the minimum relaxation rate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_03420 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Boosting thermalization of classical and quantum many-body systems Chen, Jin-Fu Rai, Kshiti Sneh Emonts, Patrick Farina, Donato Płodzień, Marcin Grzybowski, Przemyslaw Lewenstein, Maciej Tura, Jordi Quantum Physics Statistical Mechanics Understanding and optimizing the relaxation dynamics of many-body systems is essential both for foundational studies in quantum thermodynamics and for applications such as quantum simulation and quantum computing. Efficient preparation of thermal states of a many-body Hamiltonian is governed by the spectral properties of the associated Lindbladian, in particular its spectral gap, which determines the slowest relaxation rate. In this work, we develop a systematic framework for constructing Lindbladians that prepare thermal states. Our approach reveals a simple relation between the relaxation dynamics at finite and infinite temperatures. The framework is scalable to larger system sizes when implemented using tensor-network methods. We find that efficient thermalization requires that the relaxation dynamics respect the symmetries of the thermal state, which reduces the number of free parameters. By applying gradient-based optimization to the Lindbladians, we enhance the spectral gap and thereby boost thermalization. When applied to both classical and quantum spin models, our method demonstrates a substantial enhancement of the spectral gap. For larger system sizes, our approach provides a variational upper bound and enables a certified lower bound on the minimum relaxation rate. |
| title | Boosting thermalization of classical and quantum many-body systems |
| topic | Quantum Physics Statistical Mechanics |
| url | https://arxiv.org/abs/2411.03420 |