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arXiv 2209.08096cond-mat.stat-mechquant-ph
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学习效率的理论界限

Theoretical bound of the efficiency of learning

Shanhe Su, Ousi Pan, Shihao Xia, Jincan Chen, Chikako Uchiyama

AI总结:

本文提出统一热力学形式体系,通过推导比克劳修斯不等式更强的熵产生率下界,确定学习效率的一般上限,并在量子点系统和活细胞网络中验证,揭示能量与信息的基本权衡关系。

摘要:

本文提出了一种描述学习效率的统一热力学形式体系。首先,我们推导出一个比克劳修斯不等式更强的不等式,揭示了子系统熵产生率的下界。其次,将该不等式进行变换,以确定学习效率的一般上限。特别地,我们举例说明了非平衡量子点系统和活细胞网络中效率的界限。该框架提供了随机热力学过程中能量与信息之间的基本权衡关系。

英文摘要:

A unified thermodynamic formalism describing the efficiency of learning is proposed. First, we derive an inequality, which is more strength than Clausius's inequality, revealing the lower bound of the entropy-production rate of a subsystem. Second, the inequality is transformed to determine the general upper limit for the efficiency of learning. In particular, we exemplify the bound of the efficiency in nonequilibrium quantum-dot systems and networks of living cells. The framework provides a fundamental trade-off relationship between energy and information inheriting in stochastic thermodynamic processes.

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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