信息非平衡浓缩
Informational non-equilibrium concentration
AI总结：
本文研究完全简并 Hamiltonian 下乘积态的局部信息非平衡能否经全局幺正演化浓缩，证明双量子比特不可能而高维可能，并给出双三能级系统的通用最优幺正及相关资源效应。
摘要：
通过考虑具有完全简并 Hamiltonian 的系统，可以孤立地研究对热力学的信息贡献。在这一机制中，处于非平衡状态——称为信息非平衡——会仅从信息内容中提供热力学资源，例如可提取功。信息非平衡的有用性产生了获取更多此类非平衡的动机，从而引出如何浓缩它的问题：我们能否在全局封闭系统（幺正）演化下增加乘积态 ρ⊗ρ 的局部信息非平衡？我们以解析方式完全解决了这一问题，表明对于双量子比特这是不可能的，而在更高维度中总是能够找到实现这一点的态。特别地，对于双三能级系统，我们发现存在一个单一幺正变换，可对每个态实现最优浓缩，并由此揭示了一种类似 Mepmba 的效应。我们进一步讨论了该框架中的束缚资源概念、初始全局关联激活浓缩的能力，以及在浓缩纯度和内禀随机性方面的应用。
英文摘要：
Informational contributions to thermodynamics can be studied in isolation by considering systems with fully-degenerate Hamiltonians. In this regime, being in non-equilibrium -- termed informational non-equilibrium -- provides thermodynamic resources, such as extractable work, solely from the information content. The usefulness of informational non-equilibrium creates an incentive to obtain more of it, motivating the question of how to concentrate it: can we increase the local informational non-equilibrium of a product state $ρ\otimesρ$ under a global closed system (unitary) evolution? We fully solve this problem analytically, showing that it is impossible for two-qubits, and it is always possible to find states achieving this in higher dimensions. Specifically for two-qutrits, we find that there is a single unitary achieving optimal concentration for every state, for which we uncover a Mepmba-like effect. We further discuss the notion of bound resources in this framework, initial global correlations' ability to activate concentration, and applications to concentrating purity and intrinsic randomness.
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。
