Learning the closest Slater determinant

• URL: http://arxiv.org/abs/2607.20623v1
• Date: Wed, 22 Jul 2026 18:00:04 GMT
• Title: Learning the closest Slater determinant
• Abstract summary: We study the task of learning the Slater determinant with maximum fidelity to an arbitrary fermionic many-body state.<n>We apply the algorithm to the Fermi-Hubbard model, extracting the closest Slater determinant from neural quantum state solutions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning compact, interpretable descriptions of quantum many-body states is an important task in quantum science. We study the task of learning the Slater determinant with maximum fidelity to an arbitrary fermionic many-body state, with motivation from both Hartree-Fock methods and agnostic tomography. Given an $n$-fermion wavefunction built from $m$ fermionic modes, we provide classical and quantum algorithms returning a Slater determinant with fidelity within $\varepsilon$ of maximal in time $m^{\text{poly}(n,1/\varepsilon)}$. We prove matching hardness lower bounds, assuming standard complexity conjectures, along some parameter axes. Given access to quantum copies, we prove this can be accomplished with $\text{poly}(m,n,1/\varepsilon)$ copies of $ρ$. We also show that above a fidelity of $2/3$ any stationary point is the unique global maximum while below $2/3$ the optimization landscape can have spurious stationary points, and hence $2/3$ marks a transition point in the optimization landscape for this problem. We apply the algorithm to the Fermi-Hubbard model, extracting the closest Slater determinant from neural quantum state solutions. Together, our results provide algorithmic tools with provable guarantees in understanding fermionic many-body systems with classical or quantum simulation.

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