论文标题
从记忆量子随机过程中提取预测工作的引擎
Engines for predictive work extraction from memoryful quantum stochastic processes
论文作者
论文摘要
量子信息处理技术还可以从系统的固有量子特征中提取工作，此外它包含了经典的自由能。同时，计算力学科学为非马克维亚经典和量子随机过程的预测建模提供了工具。我们结合了这两个科学的工具，以开发一种技术，以从具有量子输出的非马克维亚随机过程中提取工作。我们证明，一方面，该技术可以提取更多的工作，而不是非预测性的量子提取方案，而没有量子信息处理的预测性工作提取。我们发现了记忆功效中从量子过程中提取工作的功效的相变，这是没有经典先例的。我们的作品打开了以本质上是量子的，本质上是变化的形式的机器的前景。
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.
