Measuring Accuracy and Energy-to-Solution of Quantum Fine-Tuning of Foundational AI Models
For researchers evaluating quantum machine learning, this work provides experimental evidence of favorable energy-accuracy trade-offs and establishes ETS as a scalable metric, though the results are preliminary and limited to shallow circuits.
This study measures energy-to-solution (ETS) for quantum fine-tuning of foundational AI models on a trapped-ion quantum processor, finding that QPU energy scales linearly with qubit count while classical simulation scales exponentially, with a break-even around 34 qubits. The best quantum fine-tuned model achieves a 24% classification error improvement over the best classical baseline.
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.