24 citations · 24 across the 7 of their papers we have counts for
10 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…
Quantum Enhanced Deep Reinforcement Learning for Distribution Network Reconfiguration
Daniel Germain, Mason Blanchard, Audrey Versteegen +5
Modern smart grids generate increasingly rich operational data and deploy greater numbers of remotely controlled switching assets than ever before, creating new opportunities for d…
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…
Quantum Large Language Model Fine-Tuning
Sang Hyub Kim, Jonathan Mei, Claudio Girotto +2
We introduce a hybrid quantum-classical deep learning architecture for large language model fine-tuning. The classical portion of the architecture is a sentence transformer that is…
The Price of Linear Time: Error Analysis of Structured Kernel Interpolation
Alexander Moreno, Justin Xiao, Jonathan Mei
Structured Kernel Interpolation (SKI) (Wilson et al. 2015) helps scale Gaussian Processes (GPs) by approximating the kernel matrix via interpolation at inducing points, achieving l…
Quantum Natural Language Processing
Dominic Widdows, Willie Aboumrad, Dohun Kim +2
Language processing is at the heart of current developments in artificial intelligence, and quantum computers are becoming available at the same time. This has led to great interes…