实测量子微调模型能耗与精度，34量子比特处能效超越经典方法。
Measuring Accuracy and Energy-to-Solution of Quantum Fine-Tuning of Foundational AI Models
- 在离子阱量子处理器上实测混合量子-经典微调流程的能耗与精度。
- 34量子比特时量子方案能效超越经典，最佳模型误差降低24%。
- 首次实证量子计算在小规模任务中具能耗优势，适合关注能效的量子应用研究者。
我们开展了一项关于混合量子-经典应用能量-解耗（ETS）的实验研究，通过直接测量Forte Enterprise离子阱量子处理器的功耗实现。将该方法应用于量子微调基础人工智能模型的混合量子-经典流水线，并在量子硬件上完成了端到端验证。尽管存在噪声和量子比特数量有限的问题，所得模型的准确率仍可媲美甚至超过逻辑回归和支持向量分类器等经典基线。结果表明，对于浅层电路，量子处理器能耗随量子比特数近似线性增长，而经典模拟呈指数增长，意味着在约34个量子比特时达到能效拐点。本研究中最佳量子微调模型相比最佳经典微调模型，分类误差降低约24%。我们还通过与张量网络方法的对比进一步阐释了这些发现。该工作确立了能量-解耗为可测量、可扩展的量子应用评估指标，并提供了量子计算在能效-精度权衡上具有优势的实验证据。
原文摘要 · Abstract (English)
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.
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