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

TL;DR

This study experimentally evaluates the energy-to-solution of quantum fine-tuning of AI models, demonstrating competitive accuracy and favorable energy scaling compared to classical methods, with implications for quantum advantage.

Contribution

It introduces a methodology for measuring energy-to-solution in quantum applications and provides experimental evidence of its scalability and advantages in AI model fine-tuning.

Findings

Quantum models achieve accuracy exceeding classical baselines.

QPU energy scales linearly with qubits for shallow circuits.

Break-even point at around 34 qubits for energy-to-solution.

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.