Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Physical review. B./Physical review. B

Probing the quantum critical phase with neural network wavefunctions

Haoxiang Chen, Weiluo Ren, Xiang Li, Ji Chen

Published Jun 9, 2025• 0 citations
Publisher
DOI
10.1103/dd3s-33fc

Related papers

Ab initio quantum chemistry with neural-network wavefunctions

Jan Hermann, JAMES M. SPENCER, Kenny Choo +5 more

arXiv:2208.12590•Nature Reviews Chemistry•Aug 9, 2023•131 citations

Neural-network wavefunctions

Kaitlin McCardle

Nature Computational Science•May 17, 2022•2 citations

Autoregressive neural-network wavefunctions for ab initio quantum chemistry

Thomas D. Barrett, Aleksei Malyshev, Alexander I. Lvovsky

Nature Machine Intelligence•Mar 31, 2022•92 citations

Probing quantum critical phase from neural network wavefunction

Haoxiang Chen, Weiluo Ren, Xiang Li +1 more

One-dimensional (1D) systems and models provide a versatile platform for emergent phenomena induced by strong electron correlation. In this work, we extend the newly developed real space neural network quantum Monte Carlo methods to study the quantum phase transition of electronic and magnetic properties. Hydrogen chains of different interatomic distances are explored systematically with both open and periodic boundary conditions, and fully correlated ground state many-body wavefunction is achieved via unsupervised training of neural networks. We demonstrate for the first time that neural networks are capable of capturing the quantum critical behavior of Tomonaga- Luttinger liquid (TLL), which is known to dominate 1D quantum systems. Moreover, we reveal the breakdown of TLL phase and the emergence of a Fermi liquid behavior, evidenced by abrupt changes in the spin structure and the momentum distribution. Such behavior is absent in commonly studied 1D lattice models and is likely due to the involvement of high-energy orbitals of hydrogen atoms. Our work highlights the powerfulness of neural networks for representing complex quantum phases.

arXiv:2411.19938•Nov 29, 2024•0 citations

Finding Quantum Critical Points with Neural-Network Quantum States

Remmy A. M. Zen, My Long, Tan Ryan +5 more

Frontiers in artificial intelligence and applications•Jan 1, 2020•0 citations

Identifying quantum phase transitions using artificial neural networks on experimental data

Benno S. Rem, Niklas Käming, Matthias Tarnowski +5 more

arXiv:1809.05519•Nature Physics•Jul 1, 2019•235 citations
One-dimensional (1D) systems and models provide a versatile platform for emergent phenomena induced by strong electron correlation. In this work, we extend the newly developed real space neural network quantum Monte Carlo methods to study the quantum phase transition of electronic and magnetic properties. Hydrogen chains of different interatomic distances are explored systematically with both open and periodic boundary conditions, and fully correlated ground state many-body wavefunction is achieved via unsupervised training of neural networks. We demonstrate for the first time that neural networks are capable of capturing the quantum critical behavior of Tomonaga- Luttinger liquid (TLL), which is known to dominate 1D quantum systems. Moreover, we reveal the breakdown of TLL phase and the emergence of a Fermi liquid behavior, evidenced by abrupt changes in the spin structure and the momentum distribution. Such behavior is absent in commonly studied 1D lattice models and is likely due to the involvement of high-energy orbitals of hydrogen atoms. Our work highlights the powerfulness of neural networks for representing complex quantum phases.