TetrisCNN for interpretable detection of phases of matter from experimental quantum simulator data
quant-ph, cond-mat.dis-nn, cs.LG
Submitted: 2026-09-17
Updated: 2026-09-17
Comments: 34 pages, 25 figures
License: http://creativecommons.org/licenses/by/4.0/
The gist: 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
Terminology
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.
Sources
- Machine learning applications in cold atom quantum simulators
- Superconducting Qubits: Current State of Play
- Many-Body Physics with Individually-Controlled Rydberg Atoms
- Synthetic three-dimensional atomic structures assembled atom by atom
- Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence
- Distance learning from projective measurements as an information-geometric probe of many-body physics
- Unsupervised Machine Learning for Experimental Detection of Quantum-Many-Body Phase Transitions
- Discovering quantum phenomena with Interpretable Machine Learning
- From superposition to sparse codes: interpretable representations in neural networks
- Fundamental limitations for measurements in quantum many-body systems
- Experimental demonstration of adversarial examples in learning topological phases
- Learning symmetry-protected topological order from trapped-ion experiments
- Machine learning phase transitions: Connections to the Fisher information
- Closed-Form Interpretation of Neural Network Classifiers with Symbolic Gradients
- Machine Learning Detection of Correlations in Snapshots of Ultracold Atoms in Optical Lattices
- Human-machine collaboration: ordering mechanism of rank-2 spin liquid on breathing pyrochlore lattice
- Realizing quantum Ising models in tunable two-dimensional arrays of single Rydberg atoms
- Learning interactions between Rydberg atoms
- Scaling Laws for Neural Language Models
- Learning complexity of many-body quantum sign structures through the lens of Boolean Fourier analysis
Related papers
- Reconquering Bell sampling on qudits: stabilizer learning and testing, quantum pseudorandomness bounds, and more
- Encrypted clones can leak: Classification of informative subsets in Quantum Encrypted Cloning
- Polynomial-time classical and quantum simulation of quantum impurity models
- Theory of quantum-enhanced interferometry with general Markovian light sources
- A convergent hierarchy of spectral gap certificates for qubit Hamiltonians
- Universal Bound and Phase Transition in Many-Body Fermionic Non-Gaussianity