3 papers
cs.LG2025
Large Language Model Scaling Laws for Neural Quantum States in Quantum Chemistry
Oliver Knitter, Dan Zhao, Stefan Leichenauer +1
Scaling laws have been used to describe how large language model (LLM) performance scales with model size, training data size, or amount of computational resources. Motivated by th…
cs.CE2025
Variational quantum and neural quantum states algorithms for the linear complementarity problem
Saibal De, Oliver Knitter, Rohan Kodati +3
Variational quantum algorithms (VQAs) are promising hybrid quantum-classical methods designed to leverage the computational advantages of quantum computing while mitigating the lim…
cs.LG2024
Retentive Neural Quantum States: Efficient Ansätze for Ab Initio Quantum Chemistry
Oliver Knitter, Dan Zhao, James Stokes +3
Neural-network quantum states (NQS) has emerged as a powerful application of quantum-inspired deep learning for variational Monte Carlo methods, offering a competitive alternative…