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arXiv 2605.12331quant-phcond-mat.stat-mech
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广义概率理论中的信息热力学

Information Thermodynamics in Generalized Probabilistic Theories

The University of Tokyo(东京大学);Kyushu University(九州大学)

Koki Ono, Shun Umekawa, Hiroyasu Tajima

AI总结:

本文构建了广义概率理论中的信息热力学框架,分析测量、反馈、信息擦除与热力学第二定律的关系,并探讨了SPM模型的热力学成本及熵定义对第二定律的影响。

摘要:

广义概率理论(GPTs)提供了一个统一的框架,用于描述概率物理理论,涵盖了经典理论、量子理论以及超越量子力学的假想理论。由于大多数GPTs高度不现实且远离已知物理理论,因此需要通过物理合理的原理来约束它们。其中一个最重要的原理是与热力学的一致性,这已通过涉及半透膜(SPMs)的玩具模型进行广泛研究。另一方面,信息热力学在理解经典和量子理论中测量与热力学的关系中起着核心作用,但在GPTs中仍然发展不充分。在本文中,我们构建了GPTs中的信息热力学,并提供了一个统一的框架,用于分析测量、反馈、信息擦除和热力学第二定律之间的关系。我们还制定了SPM模型的一般框架,并分析了由SPM实现的测量的热力学成本。结果表明,只要测量过程与熵非递减一致,就不可能从矛盾中提取工作,并为几种在GPTs中提出熵定义的性质推导出充分条件。此外,通过考虑违反这些条件的测量过程,我们构建了实现等温SPM循环的显式GPT系统,从中可以提取正工作。这些结果表明,违反第二定律可能源于基本熵属性的缺乏或熵定义之间的差异,并为理解GPTs中热力学与测量关系提供了统一且模型无关的基础。

英文摘要:

Generalized probabilistic theories (GPTs) provide a unified framework for describing probabilistic physical theories, encompassing classical and quantum theories as well as hypothetical theories beyond quantum mechanics. Since most GPTs are highly unrealistic and far removed from known physical theories, it is important to constrain them by physically reasonable principles. One of the most important such principles is consistency with thermodynamics, which has been extensively studied through toy models involving semipermeable membranes (SPMs) implementing measurements. On the other hand, information thermodynamics, which plays a central role in understanding the relationship between measurement and thermodynamics in classical and quantum theory, has remained largely undeveloped in GPTs. In this work, we construct information thermodynamics in GPTs and provide a unified framework for analyzing the relationship between measurement, feedback, information erasure, and the second law of thermodynamics. We also formulate a general framework for SPM models and analyze the thermodynamic cost of measurements implemented by SPMs. As a result, we show that no work can be extracted in contradiction with the second law as long as the measurement processes are consistent with entropy nondecrease, and derive sufficient conditions for this property for several entropy definitions proposed in GPTs. Moreover, by considering measurement processes violating these conditions, we construct explicit GPT systems realizing isothermal SPM cycles from which positive work can be extracted. These results demonstrate that violations of the second law can arise from the lack of fundamental entropy properties or discrepancies between entropy definitions, and provide a unified and model-independent foundation for understanding the relationship between thermodynamics and measurement in GPTs.

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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