TetrisCNN for interpretable detection of phases of matter from experimental quantum simulator data
This research introduces TetrisCNN, a convolutional architecture with differently shaped filters, to detect phases of matter in quantum simulator data. Applied to experimental snapshots of two-dimensional Ising and XY quantum simulators, the model identifies phase transitions and crossovers while expressing its latent representation and decision boundaries as symbolic formulas built from experimentally measurable spin correlators.