发现二维非平衡记忆新机制，可抗扰动存信息。
Exploring the Landscape of Non-Equilibrium Memories with Neural Cellular Automata
- 用机器学习与严格证明结合，探索二维非平衡记忆系统
- 找到能纠错、靠涨落稳定有序态的新记忆模式
- 适合对物理信息存储、复杂系统感兴趣的学者
我们研究多体记忆的景观：一类局部非平衡动力学，可在热力学长时间尺度下保留初始状态信息，即使面对任意扰动。在二维情况下，此前唯一被广泛研究的记忆是Toom规则。通过结合严格证明与机器学习方法，我们发现二维记忆的结构远比此前认知丰富。发现了纠错方式与Toom规则本质不同的记忆，具有由涨落稳定的有序相，以及仅在噪声存在时才能保持信息的特性。结果表明，物理系统可通过多种不同方式实现鲁棒的信息存储，揭示了多体记忆的物理机制远比先前认为的更丰富。本文研究的动力学交互可视化见 https://memorynca.github.io/2D。
原文摘要 · Abstract (English)
We investigate the landscape of many-body memories: families of local non-equilibrium dynamics that retain information about their initial conditions for thermodynamically long time scales, even in the presence of arbitrary perturbations. In two dimensions, the only well-studied memory is Toom's rule. Using a combination of rigorous proofs and machine learning methods, we show that the landscape of 2D memories is in fact quite vast. We discover memories that correct errors in ways qualitatively distinct from Toom's rule, have ordered phases stabilized by fluctuations, and preserve information only in the presence of noise. Taken together, our results show that physical systems can perform robust information storage in many distinct ways, and demonstrate that the physics of many-body memories is richer than previously realized. Interactive visualizations of the dynamics studied in this work are available at https://memorynca.github.io/2D.
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