Quantum Many-Body Physics

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4 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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New non-Euclidean neural quantum states from additional types of hyperbolic recurrent neural networks
Apr 27, 2026H. L. DaoNeural Quantum States Recurrent Neural Networks
From Paper to Program: Knowledge Externalization and Bottleneck Diagnosis in AI-Assisted Quantum Many-Body Programming
Apr 5, 2026Yi ZhouScientific Code Generation Code Generation Evaluation
Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China
Predicting magnetism with first-principles AI
Feb 9, 2026Max Geier, Liang FuMaterials Property Prediction Materials Science
Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Revisiting the Broken Symmetry Phase of Solid Hydrogen: A Neural Network Variational Monte Carlo Study
Dec 19, 2025Shengdu Chai, Chen Lin, Xinyang Dong +4Crystal Structure Prediction Symmetry Breaking
Interdisciplinary Center for Theoretical Physics and Information Sciences (ICTPIS), Fudan University, Shanghai 200433, China · Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China · Department of Engineering, University of Oxford, Oxford OX1 4BH, UK +4

Apr 27, 2026, quant-phJ/K move · Enter open · S save

New non-Euclidean neural quantum states from additional types of hyperbolic recurrent neural networks

H. L. Dao

Neural Quantum States Recurrent Neural Networks Quantum Many-Body Physics

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In this work, we extend the class of previously introduced non-Euclidean neural quantum states (NQS) which consists only of Poincare hyperbolic GRU, to new variants including Poincare RNN as well as Lorentz RNN and Lorentz GRU. In addition to constructing the new non-Euclidean hyperbolic NQS ansatzes, we generalize the results of our earlier work regarding the definitive outperformances delivered by hyperbolic Poincare GRU NQS when benchmarked against their Euclidean counterparts in the Variational Monte Carlo (VMC) experiments involving the Heisenberg

J_1J_2

and

J_1J_2J_3

models. Here, using larger systems consisting of 100 spins, we find that all four hyperbolic RNN/GRU NQS variants always outperform their respective Euclidean counterpart with the same architecture. In our experiments, among the four hyperbolic NQS, Lorentz RNN stands out in particular because despite having almost three times fewer parameters, it is capable of surpassing the more complex Poincare GRU and Lorentz GRU to emerge as the best overall hyperbolic NQS ansatz on many instances involving different

J_2

and (

J_2, J_3

) couplings. Given the findings from this work showing that the four newly constructed hyperbolic RNN/GRU NQS ansatzes are able to outperform the well-established Euclidean RNN/GRU NQS in Heisenberg spin models, we establish the utility and efficiency of the hyperbolic Poincare RNN/GRU and Lorentz RNN/GRU NQS for future variational studies of quantum many-body systems, especially those exhibiting a hierarchical structure in the form of the different degrees of nearest-neighbor interactions.

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Latest papers 34

New non-Euclidean neural quantum states from additional types of hyperbolic recurrent neural networks

From Paper to Program: Knowledge Externalization and Bottleneck Diagnosis in AI-Assisted Quantum Many-Body Programming

Predicting magnetism with first-principles AI

Revisiting the Broken Symmetry Phase of Solid Hydrogen: A Neural Network Variational Monte Carlo Study