Learning Minimal Representations of Fermionic Ground States

• URL: http://arxiv.org/abs/2512.11767v1
• Date: Fri, 12 Dec 2025 18:26:05 GMT
• Title: Learning Minimal Representations of Fermionic Ground States
• Authors: Felix Frohnert, Emiel Koridon, Stefano Polla,
• Abstract summary: We introduce an unsupervised machine-learning framework that discovers optimally compressed representations of quantum many-body ground states.<n>We identify minimal latent spaces with a sharp reconstruction quality threshold at $L-1$ latent dimensions, matching the system's intrinsic degrees of freedom.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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