1 citations · 1 across the 9 of their papers we have counts for
11 papers
Neural Fourier Surrogates for Data Reuploading Quantum Neural Networks
Oliver Knitter, Jonathan Mei, Sang Hyub Kim +3
For quantum machine learning, the exact boundary between classical and quantum advantage is still poorly understood. Direct comparison between quantum neural networks (QNNs) and ex…
ShamAN-Q: Shampoo Augmented NanoQuant for Sub-1-bit LLM Weights
Jonathan Mei, Sang Hyub Kim, Oliver Knitter +2
We introduce ShamAN-Q, a sub-1-bit post-training quantization method that extends NanoQuant by replacing each its diagonal reconstruction geometry with a tractable dense curvature…
Towards Scaling Quantum Fine-Tuning of Foundational Time Series Models for Classification
Sang Hyub Kim, Julien Baglio, Rajiv Krishnakumar +7
Time-series foundation models produce rich embeddings, but whether quantum models can exploit them, and how far hybrid classical-quantum architectures scale, remains unclear. We ad…
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…
Toward Neural Network Simulation of Variational Quantum Algorithms
Oliver Knitter, James Stokes, Shravan Veerapaneni
Variational quantum algorithms (VQAs) utilize a hybrid quantum-classical architecture to recast problems of high-dimensional linear algebra as ones of stochastic optimization. Desp…
Meta Variational Monte Carlo
Tianchen Zhao, James Stokes, Oliver Knitter +2
An identification is found between meta-learning and the problem of determining the ground state of a randomly generated Hamiltonian drawn from a known ensemble. A model-agnostic m…