Measuring Accuracy and Energy-to-Solution of Quantum Fine-Tuning of Foundational AI Models

2026-05-04

AI Summary

This work investigates the efficient evaluation of accuracy and energy efficiency for quantum-classical hybrid AI models on noisy, limited-scale quantum hardware. By directly measuring power consumption on the Forte Enterprise trapped-ion quantum processor, the study implements an end-to-end quantum fine-tuning pipeline and introduces “energy-to-solution” (ETS) as a core evaluation metric. Empirical results on real quantum hardware demonstrate, for the first time, that quantum fine-tuned models achieve approximately 24% lower classification error than the best classical baseline. Furthermore, QPU energy consumption scales nearly linearly with qubit count, whereas classical simulation exhibits exponential growth, leading to an ETS breakeven point at around 34 qubits. These findings establish ETS as a viable benchmark for scalable quantum AI applications.

Abstract

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. Despite noise and limited qubit counts, the resulting models achieve accuracy competitive with and exceeding classical baselines such as logistic regression and support vector classifiers. Our results show that QPU energy consumption scales approximately linearly with qubit number for shallow circuits, while classical simulation exhibits exponential scaling, indicating a break-even for ETS around 34 qubits. The classification error improvement of the best quantum fine-tuned model over the best classical fine-tuned model considered in this study is around 24%. We further contextualize these findings with comparisons to tensor network methods. This work establishes energy-to-solution as a measurable and scalable metric for evaluating quantum applications and provides experimental evidence of favorable energy-accuracy trade-offs.

Problem

• energy-to-solution
• quantum fine-tuning
• foundational AI models
• accuracy
• quantum-classical hybrid

Innovation

• energy-to-solution
• quantum fine-tuning
• hybrid quantum-classical
• trapped-ion QPU
• foundational AI models