research
The Phase Detector Wrote Its Decision as Spin Correlators
Tetris-shaped filters turned noisy simulator snapshots into symbolic, experimentally measurable formulas.
Summary
Tetris-shaped filters turned noisy simulator snapshots into symbolic, experimentally measurable formulas.
Neural networks can locate phase transitions but rarely explain their order parameters. TetrisCNN runs differently shaped convolutional filters in parallel and learns sparse latent variables expressed as spin correlators. On experimental snapshots from two-dimensional Ising and XY quantum simulators measured in several bases, it detected transitions and crossovers while turning its representation and decision boundary into symbolic formulas. The claim is interpretability on the tested systems, not automatic discovery of every phase of matter.
Why it matters
Tetris-shaped filters turned noisy simulator snapshots into symbolic, experimentally measurable formulas.
Limits and context
- The claim is interpretability on the tested systems, not automatic discovery of every phase of matter.
Key claims
Tetris-shaped filters turned noisy simulator snapshots into symbolic, experimentally measurable formulas.
Qualification: The claim is interpretability on the tested systems, not automatic discovery of every phase of matter.
Evidence: source-2026-09-19-015
Sources
- arXiv preprint 2609.20693arXiv · primary research
Corrections
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