Adiabatic transport of neural network quantum states

arXiv:2510.15030 · quant-ph, cond-mat.dis-nn · Submitted 2025-10-16 · Read on arXiv

quant-ph, cond-mat.dis-nn

Submitted: 2025-10-16

Updated: 2026-09-18

Comments: 6 pages, 4 figures (+6 pages, 3 figures in supplemental material)

Journal ref: PRX Intelligence 1, 013020 (2026)

DOI: 10.1103/khy4-5v68

Code: https://github.com/Matematija/nqs-adiabatic

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Fidelity — 13× in this paper · explained in 2 episodes
• Quantum Physics — 5× in this paper · explained in 10 episodes
• Variational Monte Carlo — 4× in this paper
• Accuracy — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Phase Transitions — 3× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Hubbard Model — 2× in this paper · explained in 3 episodes
• Ab Initio — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Domain Walls — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Hilbert Space — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Restricted Boltzmann Machine (RBM) — 1× in this paper · explained in 3 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Singular Value Decomposition (SVD) — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Machine learning and the physical sciences
• Neural-network quantum states for many-body physics
• From Architectures to Applications: A Review of Neural Quantum States
• Modern applications of machine learning in quantum sciences
• Machine learning phases of matter
• Autoregressive Neural Quantum States with Quantum Number Symmetries
• Solving the Hubbard model with Neural Quantum States
• Neural Network-Augmented Pfaffian Wave-functions for Scalable Simulations of Interacting Fermions
• A Self-Attention Ansatz for Ab-initio Quantum Chemistry
• An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking
• Deep Variational Free Energy Calculation of Hydrogen Hugoniot
• Simulating dynamics of correlated matter with neural quantum states
• Variational quantum dynamics of two-dimensional rotor models
• Ab-initio variational wave functions for the time-dependent many-electron Schr\"odinger equation
• Many-body dynamics with explicitly time-dependent neural quantum states
• Time-dependent Neural Galerkin Method for Quantum Dynamics
• Variational subspace methods and application to improving variational Monte Carlo dynamics
• Grassmann Variational Monte Carlo with neural wave functions
• Geometry and non-adiabatic response in quantum and classical systems
• Adiabatic Fine-Tuning of Neural Quantum States Enables Detection of Phase Transitions in Weight Space

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