106 citations · 184 across the 8 of their papers we have counts for
5 papers · 1 filter
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 the drive towards energy-efficient generative AI with quantum computing algorithms
Frederik F. Flöther, Jan Mikolon, Maria Longobardi
Research and usage of artificial intelligence, particularly generative and large language models, have rapidly progressed over the last years. This has, however, given rise to issu…
The state of quantum computing applications in health and medicine
Frederik F. Flöther
Medicine, including fields in healthcare and life sciences, has seen a flurry of quantum-related activities and experiments in the last few years (although biology and quantum theo…
Quantum kernels for real-world predictions based on electronic health records
Zoran Krunic, Frederik F. Flöther, George Seegan +2
In recent years, research on near-term quantum machine learning has explored how classical machine learning algorithms endowed with access to quantum kernels (similarity measures)…