提出单次测量即可准确推理的量子机器学习模型，大幅降低运行成本。
You Only Measure Once: On Designing Single-Shot Quantum Machine Learning Models
- 用概率聚合替代传统期望值输出，减少对多次测量依赖。
- 在MNIST和CIFAR-10上实现单次测量下性能优于基线模型。
- 适合资源受限场景下的量子机器学习部署，尤其利好低成本硬件应用。
量子机器学习（QML）模型通常依赖对可观测量的多次测量（采样次数）以获得可靠预测。这种对高采样预算的依赖导致推理成本和时间开销大，而量子硬件访问费用往往与采样次数成正比。本文提出You Only Measure Once（Yomo），一种简单但高效的设计，在极低采样量下实现精准推理，甚至可降至单次测量。Yomo将保罗利期望值输出替换为概率聚合机制，并引入促使预测尖锐化的损失函数。理论分析表明，Yomo规避了基于期望值模型固有的采样数缩放限制；在MNIST和CIFAR-10上的实验验证其在不同采样预算下及含去极化通道的模拟中均持续优于基线模型。该方法显著降低了部署QML的经济与计算成本，推动其向实际应用落地迈进。
原文摘要 · Abstract (English)
Quantum machine learning (QML) models conventionally rely on repeated measurements (shots) of observables to obtain reliable predictions. This dependence on large shot budgets leads to high inference cost and time overhead, which is particularly problematic as quantum hardware access is typically priced proportionally to the number of shots. In this work we propose You Only Measure Once (Yomo), a simple yet effective design that achieves accurate inference with dramatically fewer measurements, down to the single-shot regime. Yomo replaces Pauli expectation-value outputs with a probability aggregation mechanism and introduces loss functions that encourage sharp predictions. Our theoretical analysis shows that Yomo avoids the shot-scaling limitations inherent to expectation-based models, and our experiments on MNIST and CIFAR-10 confirm that Yomo consistently outperforms baselines across different shot budgets and under simulations with depolarizing channels. By enabling accurate single-shot inference, Yomo substantially reduces the financial and computational costs of deploying QML, thereby lowering the barrier to practical adoption of QML.
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