标题： 注意力是解决手性超导性的全部所需 显示英文标题

标题： Attention is all you need to solve chiral superconductivity

摘要： 最近在神经量子态方面的进展表明，量子粒子之间的关联可以通过{\it attention}有效捕捉——这是现代神经架构的基础，使神经网络能够学习物体之间的关系。 在本工作中，我们表明一种通用的自注意力费米神经网络可以通过能量最小化在吸引费米气体中找到手征$p_x \pm i p_y$超导性，{\it 不带任何关于配对的先验知识或偏见}。 超导态通过测量各种物理可观测量从优化的波函数中识别出来：成对结合能、基态的总角动量以及两体约化密度矩阵中的非对角长程序。 我们的工作为在强关联量子材料中发现非常规和拓扑超导性提供了道路。 显示英文摘要

摘要： Recent advances on neural quantum states have shown that correlations between quantum particles can be efficiently captured by {\it attention} -- a foundation of modern neural architectures that enables neural networks to learn the relation between objects. In this work, we show that a general-purpose self-attention Fermi neural network is able to find chiral $p_x \pm i p_y$ superconductivity in an attractive Fermi gas by energy minimization, {\it without prior knowledge or bias towards pairing}. The superconducting state is identified from the optimized wavefunction by measuring various physical observables: the pair binding energy, the total angular momentum of the ground state, and off-diagonal long-range order in the two-body reduced density matrix. Our work paves the way for AI-driven discovery of unconventional and topological superconductivity in strongly correlated quantum materials.