You Only Measure Once: On Designing Single-Shot Quantum Machine Learning Models
This work addresses the financial and computational cost problem for deploying QML, enabling practical adoption by reducing measurement requirements.
The paper tackled the high inference cost and time overhead in quantum machine learning (QML) models due to reliance on repeated measurements, by proposing You Only Measure Once (Yomo), which achieves accurate inference with dramatically fewer measurements, down to single-shot, and outperforms baselines on MNIST and CIFAR-10 datasets.
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.