arXiv Machine Learning July 17, 2026 By H. L. Dao

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

Read the original on arXiv Machine Learning →

arXiv:2604. 24337v2 Announce Type: replace-cross Abstract: 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.

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arXiv Machine Learning

Aug 4

New non-Euclidean neural quantum states from hyperbolic Lorentz recurrent architectures

arXiv:2604. 24337v3 Announce Type: replace-cross Abstract: In this work, we construct new non-Euclidean neural quantum states (NQS) based on hyperbolic Lorentz recurrent architectures (RNN/GRU).

By H. L. Dao

arXiv Machine Learning

Jun 25

Two-dimensional Hyperbolic RNN Neural Quantum State

arXiv:2606. 25600v1 Announce Type: cross Abstract: In the first part of this work, we construct the first type of two-dimensional (2D) hyperbolic neural quantum state (NQS) in the form of the Lorentz 2DRNN (Recurrent Neural Network) and benchmark its performance against the Euclidean 2DRNN in the paradigmatic $N\times N$ 2D Transverse Field Ising Model (2DTFIM) setting with different lattice sizes up to $N=12$ and at different transverse magnetic field strengths.

By H. L. Dao

benchmarks

arXiv Machine Learning

Sep 23

Hyperbolic Restricted Boltzmann Machine Neural Quantum State

arXiv:2609. 26032v1 Announce Type: cross Abstract: We construct the first type of non-Euclidean non-autoregressive neural quantum state (NQS) in the form of the hyperbolic Restricted Boltzmann Machine (HRBM), which is studied in the variational Monte-Carlo (VMC) setting of the Quantum Sherrington-Kirkpatrick (QSK) model whose ground state exhibits volume-law entanglement.

By H. L. Dao

arXiv Machine Learning

Jul 3

One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective

arXiv:2607. 02292v1 Announce Type: new Abstract: Neural quantum states (NQS) provide a flexible and scalable framework for approximating quantum many-body wavefunctions.

By Juan Agust\'in Duque, Sergio Garc\'ia Heredia, Vinicius Hernandes, Eli\v{s}ka Greplov\'a, Thomas Spriggs, Aaron Courville, Anna Dawid

reinforcement-learning fine-tuning

arXiv AI

Aug 13

Hamilton-Zero: A Neural Tensor-Network Foundation Model for Ground States of Arbitrary Quadratic Qubit Hamiltonians

arXiv:2608. 11911v1 Announce Type: cross Abstract: A central promise of useful quantum advantage is the ability to compute ground states of Hamiltonian systems beyond the reach of classical simulation methods.

By Timothy Heightman, Elena Orlova, Philip Mantrov, Aleksei Ustimenko

llms reinforcement-learning fine-tuning

arXiv AI

Jun 2

Universal Quantum Transformer

arXiv:2606. 00045v1 Announce Type: new Abstract: Classical continuous-space neural networks fundamentally struggle to lock into exact mathematical symmetries, such as modular arithmetic and non-commutative algebra.

By Sungyong Chung, Alireza Talebpour

llms rag safety

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