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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…