TetrisCNN is a convolutional neural network that uses parallel branches of differently shaped filters to learn sparse, interpretable latent representations directly from spin correlators. Applied to experimental snapshots of two-dimensional Ising and XY quantum simulators, it detects phase transitions and crossovers while expressing its decision boundaries as symbolic formulas built from measurable spin correlators. This approach bridges the gap between black‑box neural networks and physically interpretable models, enabling automated discovery of new phases of matter from realistic, noisy experimental data.
TetrisCNN is a convolutional neural network that uses parallel branches of differently shaped filters to learn sparse, interpretable latent representations directly in terms of spin correlators. Applied to experimental snapshots from two-dimensional Ising and XY quantum simulators measured in multiple bases, the network detects phase transitions and crossovers while expressing its decision boundaries as symbolic formulas built from experimentally measurable spin correlators. This approach bridges the gap between black‑box neural network methods and physically interpretable models, enabling automated detection of phases of matter from realistic, noisy experimental data.
By Kacper Cybi\'nski, Bj\"orn van Zwol, James Enouen, Guillaume Bornet, Thierry Lahaye, Antoine Browaeys, Antoine Georges, Anna Dawid
The paper introduces a hybrid quantum–classical regression framework that uses a lightweight classical embedding as a learnable geometric preconditioner to improve the conditioning of a downstream variational quantum circuit. It further incorporates a curriculum optimization protocol that gradually increases circuit depth and switches from SPSA-based exploration to Adam-based fine‑tuning. Experiments on PDE‑informed and standard regression datasets show that this approach consistently outperforms pure QNN baselines, yielding more stable convergence and reduced structured errors, especially in data‑limited regimes.
By Qingyu Meng, Yangshuai Wang
arXiv:2607. 21409v1 Announce Type: cross Abstract: A central challenge in quantum machine learning is understanding the scaling behavior of parameterized quantum circuits (PQCs).
By Marie Kempkes, Elies Gil-Fuster, Carlos Bravo-Prieto, Aroosa Ijaz, Alissa Wilms, Jens Eisert, Evert van Nieuwenburg, Vedran Dunjko
arXiv:2608. 00850v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have emerged as a versatile approach for solving nonlinear partial differential equations (PDEs), yet achieving high accuracy efficiently using these techniques remains challenging for high-dimensional or multiscale systems.
By Fabio Pereira dos Santos, Renato Portugal, J\'ulio de Castro Vargas Fernandes, Lucas Timotheo Sanches
arXiv:2511. 17228v2 Announce Type: replace-cross Abstract: Artificial intelligence in dynamic, real-world environments requires the capacity for continual learning.
By Yu-Qin Chen, Shi-Xin Zhang