AI RESEARCH
Measuring Accuracy and Energy-to-Solution of Quantum Fine-Tuning of Foundational AI Models
arXiv CS.AI
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ArXi:2605.02798v1 Announce Type: cross 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 trapped-ion quantum processor. We apply this methodology to a hybrid quantum-classical pipeline for quantum fine-tuning of foundational AI models, and validate the approach end-to-end on quantum hardware.