Foundation Neural-Network Quantum States for Molecular Potential Energy Surfaces in Second Quantization
physics.chem-ph, cs.LG, quant-ph
Submitted: 2026-09-29
Updated: 2026-09-29
Terminology
Sources
- Solving the Quantum Many-Body Problem with Artificial Neural Networks
- Ab-initio quantum chemistry with neural-network wavefunctions
- Scalable neural quantum states architecture for quantum chemistry
- Neural network backflow for ab-initio quantum chemistry
- Foundation Neural-Networks Quantum States as a Unified Ansatz for Multiple Hamiltonians
- Corresponding Active Orbital Spaces along Chemical Reaction Paths
- Sampling-free Inference for Ab-Initio Potential Energy Surface Networks
- Ab-Initio Solution of the Many-Electron Schr\"odinger Equation with Deep Neural Networks
- A Self-Attention Ansatz for Ab-initio Quantum Chemistry
- Autoregressive Neural Quantum States with Quantum Number Symmetries
- Eigenvector continuation with subspace learning
- A theory of quantum subspace diagonalization
- Trimmed Sampling Algorithm for the Noisy Generalized Eigenvalue Problem
- Enhancing the Expressivity of Variational Neural, and Hardware-Efficient Quantum States Through Orbital Rotations
- Neural Quantum States and Peaked Molecular Wave Functions: Curse or Blessing?
- Recurrent Neural Network Wave Functions
- FiLM: Visual Reasoning with a General Conditioning Layer
- Layer Normalization
- NNQS-Transformer: an Efficient and Scalable Neural Network Quantum States Approach for Ab initio Quantum Chemistry
- Recent developments in the PySCF program package
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