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arXiv 2411.03420quant-phcond-mat.stat-mech
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促进经典与量子多体系统的热化

Boosting thermalization of classical and quantum many-body systems

Jin-Fu Chen, Kshiti Sneh Rai, Patrick Emonts, Donato Farina, Marcin Płodzień, Przemyslaw Grzybowski, Maciej Lewenstein, Jordi Tura

AI总结:

本研究针对多体系统热态制备问题,开发了构建Lindbladian算符的系统框架,通过梯度优化增强谱隙以促进热化,在经典与量子自旋模型中实现谱隙显著提升,并为大系统提供弛豫速率的变分上界与认证下界。

摘要:

理解和优化多体系统的弛豫动力学对于量子热力学的基础研究以及量子模拟和量子计算等应用都至关重要。多体哈密顿量热态的高效制备由相关 Lindbladian 算符的谱特性决定,尤其是其谱隙,谱隙决定了最慢的弛豫速率。在本研究中,我们开发了一个系统框架用于构建制备热态的 Lindbladian 算符。我们的方法揭示了有限温度与无限温度下弛豫动力学之间的简单关系。当使用张量网络方法实现时,该框架可扩展到更大的系统尺寸。我们发现,高效热化要求弛豫动力学尊重热态的对称性,这减少了自由参数的数量。通过对 Lindbladian 算符应用基于梯度的优化,我们增强了谱隙,从而促进了热化。将我们的方法应用于经典和量子自旋模型时,均显示出谱隙的显著增强。对于更大的系统尺寸,我们的方法提供了变分上界,并实现了最小弛豫速率的认证下界。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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