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Trapped Ion Quantum Computing

Learning Minimal Representations of Fermionic Ground States

arXiv
Authors: Felix Frohnert, Emiel Koridon, Stefano Polla

Year

2025

Paper ID

36612

Status

Preprint

Abstract Read

~2 min

Abstract Words

93

Citations

N/A

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.

Why This Paper Matters

  • This paper contributes to the Trapped-Ion Quantum Computing research area in the Quantum Articles archive.
  • It adds a 2025 reference point for readers tracking recent quantum research.
  • We introduce an unsupervised machine-learning framework that discovers optimally compressed representations of quantum many-body ground states.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2512.11767 [2] arXiv https://arxiv.org/abs/2512.11767 [3] Publisher https://arxiv.org/abs/2512.11767

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