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arXiv · 2512.11767

Learning Minimal Representations of Fermionic Ground States

Felix Frohnert·Emiel Koridon·Stefano Polla
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Abstract

We introduce an unsupervised machine-learning framework that discovers optimally compressed representations of quantum many-body ground states. Using an autoencoder neural network architecture on data from $L$-site Fermi-Hubbard models, we identify minimal latent spaces with a sharp reconstruction quality threshold at $L-1$ latent dimensions, matching the system's intrinsic degrees of freedom. We demonstrate the use of the trained decoder as a differentiable variational ansatz to minimize energy directly within the latent space. Crucially, this approach circumvents the $N$-representability problem, as the learned manifold implicitly restricts the optimization to physically valid quantum states.

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Felix Frohnert, Emiel Koridon, Stefano Polla. 2025-12-12. Learning Minimal Representations of Fermionic Ground States. https://doi.org/10.1103/qsl5-cyq2

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