Improved Ground State Estimation in Quantum Field Theories via Normalising Flow-Assisted Neural Quantum States
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916793871761408 |
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| author | Ngairangbam, Vishal S. Spannowsky, Michael Sypchenko, Timur |
| author_facet | Ngairangbam, Vishal S. Spannowsky, Michael Sypchenko, Timur |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_12128 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improved Ground State Estimation in Quantum Field Theories via Normalising Flow-Assisted Neural Quantum States Ngairangbam, Vishal S. Spannowsky, Michael Sypchenko, Timur Quantum Physics Machine Learning High Energy Physics - Lattice High Energy Physics - Phenomenology 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. |
| title | Improved Ground State Estimation in Quantum Field Theories via Normalising Flow-Assisted Neural Quantum States |
| topic | Quantum Physics Machine Learning High Energy Physics - Lattice High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2506.12128 |