arXiv (OAI Expanded)
open access
Scaling Neural Network Quantum States for Ab Initio Quantum Chemistry
Neural-network quantum states (NNQSs) can represent many-electron wave functions without explicitly enumerating the determinant space, but their accuracy depends jointly on model size and variational-optimization effort. Here we characterize this dependence for a physics-conditioned autoregressive N
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Scaling Neural Network Quantum States for Ab Initio Quantum Chemistry#925312
- Neural Quantum States Based on Selected Configurations.#174549
- A Deterministic Framework for Neural Network Quantum States in Quantum Chemistry#652190
- A Deterministic Framework for Neural Network Quantum States in Quantum Chemistry#650737
- Nonorthogonal variational quantum simulation for quantum chemistry#1077266
Record · ID 921076
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive