具有记忆效应的重复相互作用模型中的热化
Thermalization in a repeated-interaction model with memory effects
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中文总结 AI 辅助
本研究通过重复相互作用模型，探讨记忆效应对量子比特热化的影响，发现系统收敛于热态但呈非单调趋近，并存在类似Mpemba的逆转现象，且这些现象依赖于初始相干性或布居数不平衡。
中文摘要 AI 辅助
我们研究了记忆如何影响重复相互作用模型中量子比特的热化。在我们的模型中，一个记忆量子比特与系统反复相互作用，而两个量子比特都与新鲜的热辅助量子比特发生碰撞。我们推导了它们布居数和相互相干性的精确闭合演化方程，并通过因果中断操作展示了记忆效应。尽管系统收敛到目标热态，但热化行为表现出非单调的趋近过程。我们还发现了一种类似Mpemba的逆转现象，其中热化所需的碰撞次数对目标态温度呈非单调依赖。在所考虑的初始态族中，这两种现象都需要系统与记忆之间的初始相干性或它们激发态布居数的不平衡。
英文摘要
We study how memory affects the thermalization of a qubit in a repeated-interaction model. In our model, a memory qubit interacts repeatedly with the system, while both qubits undergo collisions with fresh thermal ancillas. We derive exact closed evolution equations for their populations and mutual coherence, and demonstrate memory effects using a causal-break operation. Although the system converges to the target thermal state, the thermalization behavior shows non-monotonic approach. We also find a Mpemba-like reversal, in which the number of collisions required for thermalization depends non-monotonically on the temperature of the target state. Within the initial-state family considered, both phenomena require initial coherence between the system and memory or an imbalance in their excited-state populations.
发表机构
- Graduate School of Science and Technology, Keio University（庆应义塾大学理工学部）
- Quantum Computing Center, Keio University（庆应义塾大学量子计算中心）
- Research and Development Group, Hitachi, Ltd.（日立株式会社研发集团）
- Keio University Sustainable Quantum Artificial Intelligence Center (KSQAIC), Keio University（庆应义塾大学可持续量子人工智能中心）
机构由 AI 辅助整理，请以论文原文为准。
