127 citations · 130 across the 4 of their papers we have counts for
5 papers
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…
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…
Accelerating computational materials discovery with artificial intelligence and cloud high-performance computing: from large-scale screening to experimental validation
Chi Chen, Dan Thien Nguyen, Shannon J. Lee +8
High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and…
Overcoming the Size Limit of First Principles Molecular Dynamics Simulations with an In-Distribution Substructure Embedding Active Learner
Lingyu Kong, Jielan Li, Lixin Sun +7
Large-scale first principles molecular dynamics are crucial for simulating complex processes in chemical, biomedical, and materials sciences. However, the unfavorable time complexi…