From sparse quantum-computing data to atomistic simulation with universal machine-learning interatomic potentials

arXiv:2609.21536 · physics.chem-ph, quant-ph · Submitted 2026-09-18 · Read on arXiv

physics.chem-ph, quant-ph

Submitted: 2026-09-18

Updated: 2026-09-18

Code: https://github.com/agrossfield/wham

Terminology

• Fine-Tuning — 29× in this paper · explained in 5 episodes
• Fidelity — 6× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Alloy — 4× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Cluster expansion — 2× in this paper · explained in 2 episodes
• Ab Initio — 1× in this paper · explained in 1 episode
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Hilbert Space — 1× in this paper · explained in 2 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Hyperparameters — 1× in this paper · explained in 2 episodes
• Langevin Dynamics — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Variational Quantum Eigensolver (VQE) — 1× in this paper · explained in 5 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Towards Chemically Accurate and Scalable Quantum Simulations on IQM Quantum Hardware: A Quantum-HPC Hybrid Approach
• Enhancing quantum computations with the synergy of auxiliary field quantum Monte Carlo and computational basis tomography
• Coupled cluster method tailored by quantum selected configuration interaction
• Auxiliary-field quantum Monte Carlo method with quantum selected configuration interaction
• Integrating Classical and Quantum Software for Enhanced Simulation of Realistic Chemical Systems
• Quantum Computing Enabled ab initio Molecular Dynamics Simulations
• MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures
• Taming Multi-Domain, -Fidelity Data: Towards Foundation Models for Atomistic Scale Simulations
• Quantum computing for accurate large-scale electronic-structure calculations: DFT-embedded, post-processed quantum-selected configuration interaction
• Fine-tuning MLIP foundation models: strategies for accuracy and transferability

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