Improved Ground State Estimation in Quantum Field Theories via Normalising Flow-Assisted Neural Quantum States

• URL: http://arxiv.org/abs/2506.12128v1
• Date: Fri, 13 Jun 2025 18:00:01 GMT
• Title: Improved Ground State Estimation in Quantum Field Theories via Normalising Flow-Assisted Neural Quantum States
• Authors: Vishal S. Ngairangbam, Michael Spannowsky, Timur Sypchenko,
• Abstract summary: We propose a hybrid variational framework that enhances Neural Quantum States (NQS) with a Normalising Flow-based sampler.<n>Our approach decouples the sampling task from the variational ansatz by learning a continuous flow model that targets a discretised, amplitude-supported subspace of the Hilbert space.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a hybrid variational framework that enhances Neural Quantum States (NQS) with a Normalising Flow-based sampler to improve the expressivity and trainability of quantum many-body wavefunctions. Our approach decouples the sampling task from the variational ansatz by learning a continuous flow model that targets a discretised, amplitude-supported subspace of the Hilbert space. This overcomes limitations of Markov Chain Monte Carlo (MCMC) and autoregressive methods, especially in regimes with long-range correlations and volume-law entanglement. Applied to the transverse-field Ising model with both short- and long-range interactions, our method achieves comparable ground state energy errors with state-of-the-art matrix product states and lower energies than autoregressive NQS. For systems up to 50 spins, we demonstrate high accuracy and robust convergence across a wide range of coupling strengths, including regimes where competing methods fail. Our results showcase the utility of flow-assisted sampling as a scalable tool for quantum simulation and offer a new approach toward learning expressive quantum states in high-dimensional Hilbert spaces.

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