6 papers
Quantum Parity Representations: Learnable Basis Discovery, Encoders, and Shadow Deployment
Sang Hyub Kim, Oliver Knitter, Jonathan Mei +4
We study parity features as representations that can be evaluated entirely classically once the binary or quantized input representation and parity words are fixed, particularly wh…
Measuring Accuracy and Energy-to-Solution of Quantum Fine-Tuning of Foundational AI Models
Oliver Knitter, Sang Hyub Kim, Maximilian Wurzer +7
We present an experimental study of energy-to-solution (ETS) of hybrid quantum-classical applications, enabled by direct instrumentation of power consumption of a Forte Enterprise…
TorchQuantumDistributed
Oliver Knitter, Jonathan Mei, Masako Yamada +1
TorchQuantumDistributed (tqd) is a PyTorch-based [Paszke et al., 2019] library for accelerator-agnostic differentiable quantum state vector simulation at scale. This enables studyi…
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…
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…
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…