AI 中文总结
本文提出TetrisCNN，一种可解释卷积网络，直接从实验量子模拟器快照中学习自旋关联函数表示，检测相变并以符号公式表达决策边界，实现可解释的物相识别。
AI 中文摘要
检测物质相通常依赖于识别正确的序参量——对于未知的相变，这一任务仍然非常困难，传统上依赖于物理直觉和经验猜测。近年来，神经网络提供了一种替代途径，可以在没有任何先验物理知识的情况下定位已知模型中的相变。然而，这些方法仍然是黑箱，只能识别相而无法阐明其性质。此外，当面对现实的、含噪声的实验数据时，它们常常难以应对，而这些数据构成了物理学中自动化方法的最终测试平台。在此，我们通过引入TetrisCNN来弥合这些视角，TetrisCNN是一种卷积架构，具有并行分支的不同形状滤波器，类似于俄罗斯方块，直接以自旋关联函数的形式学习稀疏、可解释的潜在表示。将该网络应用于在多个基下测量的二维Ising和XY量子模拟器的实验快照，网络不仅能检测相变和交叉，还能将其潜在表示和决策边界表达为由实验可测量的自旋关联函数构成的符号公式。这一框架为将可解释神经网络与量子模拟器集成以发现和理解新的物质相开辟了道路。
英文摘要
Detecting phases of matter in general relies on identifying the correct order parameter - a task that remains notoriously difficult for unknown transitions and traditionally is guided by physical intuition and educated guess. Neural networks have recently offered an alternative route by locating phase transitions in known models without any a priori physical knowledge. Yet these approaches remain black boxes and only identify phases without elucidating their properties. Moreover, they often struggle when confronted with realistic, noisy experimental data, which constitute the ultimate testbed for automated methods in physics. Here, we bridge these perspectives by introducing TetrisCNN, a convolutional architecture with parallel branches of differently shaped filters, reminiscent of Tetris blocks, that learns sparse, interpretable latent representations directly in terms of spin correlators. Applied to experimental snapshots of two-dimensional Ising and XY quantum simulators measured in multiple bases, the network not only detects phase transitions and crossovers but also expresses its latent representation and decision boundaries as symbolic formulas built from experimentally measurable spin correlators. This framework opens the way to integrating interpretable neural networks with quantum simulators to uncover and understand new phases of matter.
Comments34 pages, 25 figures
