Overview

Neural quantum states represent the wavefunction of interacting quantum systems with neural networks, then use variational Monte Carlo to estimate energies and observables without storing every state amplitude. Approaches include autoregressive recurrent networks, transformers, restricted Boltzmann machines, and determinant-based models, applied to spin lattices, strongly correlated electrons, and molecular potential-energy surfaces. Current work improves these models through geometry-aware architectures, parameter sharing across system sizes or molecular geometries, and better gradient estimators, sampling, and optimization. A central tension is that expressive models can capture complex correlations and transfer between systems, but reliable training, unbiased sampling, and guarantees of generalization remain difficult—especially near phase transitions and in topologically nontrivial systems.

Latest papers

CardsList

25 papers3 papers in the last four weeks, down 50% on the four weeks before. 0.0% of all new papers.

  1. NucleoNet: A Nuclear-Aware Determinant Ansatz for Multi-Geometry Neural Variational Monte Carlo

    Oct 9, 2026Qiao Gao, Yang Huang, Enze Hou +3Variational Monte CarloComputational Quantum Chemistry

  2. Generalization of Transformer-Based Neural Quantum States via In-Context Learning

    Oct 2, 2026Zhen Qin, Qing Qu, Alfred O. HeroNeural Quantum StatesNeural Network Generalization

  3. Foundation Neural-Network Quantum States for Molecular Potential Energy Surfaces in Second Quantization

    Sep 29, 2026Lizhong Fu, Jianan Wei, Wenguan Wang +1Neural Quantum StatesComputational Quantum Chemistry

  4. Apparent Compression, Real Stability: The Intrinsic Dimension of Learning a Quantum Wavefunction

    Sep 27, 2026Lu Wei, Yufeng Wang, Chenfeng Cao +1Variational Monte CarloIntrinsic Dimensionality

  5. Stochastic Reconfiguration as Statistical Filtering for Overparameterized Neural Quantum States

    Sep 20, 2026Tak HurOverparameterized Neural NetworkNeural Quantum States

  6. Gradient-estimator design overcomes trainability barriers in neural-network-based variational optimization

    Sep 16, 2026Yi-Ran Xue, Rui Wang, Baigeng Wang +1Variance ReductionNeural Quantum States

  7. Topology Obstructs Pure Foundation Neural Quantum States

    Sep 7, 2026Timothy Heightman, Elena Orlova, Philip Mantrov +1Neural Quantum StatesQuantum Many-Body Physics

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

    Aug 12, 2026Timothy Heightman, Elena Orlova, Philip Mantrov +1Tensor NetworksNeural Quantum States

  9. Enhanced Neural Quantum State via Annealed Gradient Descent

    Jul 21, 2026Shiwei Zhou, Yiming Huang, Xiao Yuan +1Neural Quantum StatesGradient Descent

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

    Jul 2, 2026Juan Agustín Duque, Sergio García Heredia, Vinicius Hernandes +4Neural Quantum StatesSecond-Order Optimization

  11. Mechanistic Interpretability and Causal Feature Steering of Neural Quantum States via Sparse Autoencoders

    Jul 1, 2026Zihao Qi, Christopher EarlsNeural Quantum StatesMechanistic Interpretability

  12. Holographic Quantum Transformer: A Generalist Neuro-Symbolic Architecture for Solving Frustrated Systems via Generative Attention

    Jul 1, 2026Xingran Guo, Tiaojie Xiao, Jie Liu +1Neural Quantum StatesZero-Shot Learning

  13. Low-variance estimators overcome the phase-gradient bottleneck in complex-valued neural quantum states

    Jun 11, 2026Yi-Ran Xue, Rui Wang, Baigeng Wang +1Neural Quantum StatesVariational Monte Carlo

  14. Comment on "Spin-1/2 Kagome Heisenberg Antiferromagnet: Machine Learning Discovery of the Spinon Pair-Density-Wave Ground State"

    May 21, 2026Helia Kamal, Dominik Kufel, DinhDuy Vu +2Neural Quantum StatesMarkov Chain Monte Carlo

  15. Parallel Scan Recurrent Neural Quantum States for Scalable Variational Monte Carlo

    May 13, 2026Ejaaz Merali, Mohamed Hibat-Allah, Mohammad Kohandel +2Neural Quantum StatesVariational Monte Carlo

  16. QERNEL: a Scalable Large Electron Model

    Apr 28, 2026Khachatur Nazaryan, Liang FuNeural Quantum StatesMaterials Science

  17. Geometry-Induced Long-Range Correlations in Recurrent Neural Network Quantum States

    Apr 9, 2026Asif Bin Ayub, Amine Mohamed Aboussalah, Mohamed Hibat-AllahNeural Quantum StatesRecurrent Neural Networks

  18. Expressibility of neural quantum states: a Walsh-complexity perspective

    Mar 27, 2026Taige WangNeural Quantum StatesNeural Network Expressivity

  19. Excited Pfaffians: Generalized Neural Wave Functions Across Structure and State

    Mar 15, 2026Nicholas Gao, Till Grutschus, Frank Noé +1Variational Monte CarloNeural Quantum States

  20. Large Electron Model: A Universal Ground State Predictor

    Mar 2, 2026Timothy Zaklama, Max Geier, Liang FuQuantum Many-Body PhysicsNeural Quantum States

  21. Coupled Cluster con MōLe: Molecular Orbital Learning for Neural Wavefunctions

    Feb 23, 2026Luca Thiede, Abdulrahman Aldossary, Andreas Burger +9Quantum ChemistryComputational Quantum Chemistry