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20232026
most citedOvercoming the Size Limit of First Principles Molecular Dynamics Simulations with an In-Distribution Substructure Embedding Active Learner

3 citations · 3 across the 3 of their papers we have counts for

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quant-ph2026

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…

quant-ph2026

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…