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arXiv 2207.03480quant-phcond-mat.stat-mech
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从有记忆量子随机过程中进行预测性功提取的引擎

Engines for predictive work extraction from memoryful quantum stochastic processes

Ruo Cheng Huang, Paul M. Riechers, Mile Gu, Varun Narasimhachar

AI总结:

本文结合量子信息处理与计算力学,提出从带量子输出的非马尔可夫过程中预测性提取功的技术,证明其优于两类基线,并发现无经典对应的记忆效能相变。

摘要:

量子信息处理技术使得除了从系统所含的经典自由能中提取功之外,还能够从系统固有的量子特征中提取功。与此同时,计算力学科学为非马尔可夫经典和量子随机过程的预测性建模提供了工具。我们将这两门科学的工具结合起来,开发了一种从具有量子输出的非马尔可夫随机过程中进行预测性功提取的技术。我们证明,一方面,该技术能够比非预测性的量子功提取协议提取更多的功;另一方面,也优于不使用量子信息处理的预测性功提取。我们发现了在从量子过程中提取功时记忆效能的一种相变,这在经典情形中没有先例。我们的工作开启了这样一种机器的前景:这些机器以本质上量子化、本质上随时间变化的形式利用环境自由能。

英文摘要:

Quantum information-processing techniques enable work extraction from a system's inherently quantum features, in addition to the classical free energy it contains. Meanwhile, the science of computational mechanics affords tools for the predictive modeling of non-Markovian classical and quantum stochastic processes. We combine tools from these two sciences to develop a technique for predictive work extraction from non-Markovian stochastic processes with quantum outputs. We demonstrate that this technique can extract more work than non-predictive quantum work extraction protocols, on one hand, and predictive work extraction without quantum information processing, on the other. We discover a phase transition in the efficacy of memory for work extraction from quantum processes, which is without classical precedent. Our work opens up the prospect of machines that harness environmental free energy in an essentially quantum, essentially time-varying form.

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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