QuantumComputingInfo
Researchvia arXiv quant-ph

Learning the closest Slater determinant

arXiv:2607.20623v1 Announce Type: new Abstract: Learning compact, interpretable descriptions of quantum many-body states is an important task in quantum science...

Quantum Editorial Team
July 24, 2026
1 min read
This article has been aggregated from arXiv quant-ph. You can read the original publication at https://arxiv.org/abs/2607.20623.
AI Insights

Get a 3-second summary of this article

Learning the closest Slater determinant

Source: Originally published on arXiv quant-ph on July 24, 2026.

arXiv:2607.20623v1 Announce Type: new 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...


To read the full article, visit the original source page:

Read the full article on arXiv quant-ph →

#research#algorithms

Comments are not configured yet. Set up Giscus environment variables to enable discussions.

Required: NEXT_PUBLIC_GISCUS_REPO, NEXT_PUBLIC_GISCUS_REPO_ID, NEXT_PUBLIC_GISCUS_CATEGORY, NEXT_PUBLIC_GISCUS_CATEGORY_ID

Related Articles

HX
Research

A Quantum Reservoir for Neurodynamical Forecasting

arXiv:2608.00139v1 Announce Type: new Abstract: Forecasting neural activity from short recordings remains a fundamental challenge. Reservoir computing may offer...

Aug 4, 20261 min
via arXiv quant-ph
Research

LLM-Guided Initialization for Accelerated Hybrid Quantum-Classical Medical Image Classification

arXiv:2607.27262v1 Announce Type: new Abstract: Variational quantum algorithms often encounter barren plateaus, where cost gradients decay rapidly with increasi...

Jul 31, 20261 min
via arXiv quant-ph
HX
Research

Machine Learning of Quantum Entanglement from Noisy Measurements

arXiv:2607.22853v1 Announce Type: new Abstract: In this work, we investigate the application of Machine Learning (ML) algorithms to the identification and quant...

Jul 28, 20261 min
via arXiv quant-ph