Author

Elena Orlova

University of Chicago

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#artificial intelligence Preprint Sep 2026

Topology Obstructs Pure Foundation Neural Quantum States

Foundation models for ground states in spin-1/2 systems are a promising method for problems ranging from quantum chemistry to identifying new phase diagrams. Nearly all such models are currently pure-states that condition on the Hamiltonian's parameters, whose Monte Carlo samples give energy estimates according to the...

Timothy Heightman, Elena Orlova, Philip Mantrov et al. · 0 citations

Preprint Aug 2026

Hamilton-Zero: A Neural Tensor-Network Foundation Model for Ground States of Arbitrary Quadratic Qubit Hamiltonians

It is demonstrated that this problem can be effectively amortized across an arbitrary and universal set of Hamiltonians by a foundation model with $\sim0.5$B variational parameters, trained with contemporary techniques from large language models and deep reinforcement learning.

Timothy Heightman, Elena Orlova, Philip Mantrov et al. · 1 citation · ⚡1