论文标题

通过有限内存效应进行量子冷却的指数改进

Exponential improvement for quantum cooling through finite-memory effects

论文作者

Taranto, Philip, Bakhshinezhad, Faraj, Schüttelkopf, Philipp, Clivaz, Fabien, Huber, Marcus

论文摘要

量子技术的实际实现需要高度纯度的状态制备 - 或从热力学术语中,温度非常低。鉴于有限资源,热力学的第三定律禁止完美冷却。但是,已经得出了与量子热机的反复相互作用的系统的渐近基态种群的可实现的上限。这些界限适用于无内存(马尔可夫)设置,在该设置中,每个制冷步骤都独立于先前的步骤进行。在这里,我们扩展了此框架,以研究记忆对量子冷却的影响。通过通过允许马尔可夫嵌入的广义碰撞模型引入存储机制,我们得出了可实现的界限,该界限比无内存情况提供了指数优势。对于量子位,我们的界限与热浴算法冷却相吻合,我们的框架将其推广到任意维度。最后,我们描述了胜过所有标准过程的自适应阶梯最佳协议。

Practical implementations of quantum technologies require preparation of states with a high degree of purity---or, in thermodynamic terms, very low temperatures. Given finite resources, the Third Law of thermodynamics prohibits perfect cooling; nonetheless, attainable upper bounds for the asymptotic ground state population of a system repeatedly interacting with quantum thermal machines have been derived. These bounds apply within a memoryless (Markovian) setting, in which each refrigeration step proceeds independently of those previous. Here, we expand this framework to study the effects of memory on quantum cooling. By introducing a memory mechanism through a generalized collision model that permits a Markovian embedding, we derive achievable bounds that provide an exponential advantage over the memoryless case. For qubits, our bound coincides with that of heat-bath algorithmic cooling, which our framework generalizes to arbitrary dimensions. We lastly describe the adaptive step-wise optimal protocol that outperforms all standard procedures.

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