SOURCE-LINKED INTELLIGENCE

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

arXiv · AI, language, vision and robotics · article · Sep 17, 2026 · UTC

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

Read original source ↗ Open in workspace

recordType

paper

region

Global