TetrisCNN for interpretable detection of phases of matter from experimental quantum simulator data

2026-09-17

AI Summary

本文提出TetrisCNN，一种具有不同形状滤波器的卷积架构，用于从实验量子模拟器数据中检测物质相，并通过可解释的潜在表示揭示其属性。

Abstract

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.

Problem

• phases of matter
• order parameter
• neural networks
• experimental data
• interpretable

Innovation

• TetrisCNN
• interpretable latent representations
• spin correlators
• phase transitions