用神经网络集成实现量子参数估计与不确定性量化，支持实时应用。
Quantum parameter estimation with uncertainty quantification from continuous measurement data using neural network ensembles
- 采用深度集成模型同时优化参数估计与不确定性预测。
- 在保持高精度的同时，实现可靠的不确定性评估，不降低估计性能。
- 可检测实验数据漂移，推理速度远超传统贝叶斯方法。
我们证明，深度神经网络集成（deep ensembles）可用于量子参数估计，并能提供参数估计的不确定性量化，这是贝叶斯推断的关键优势，而传统机器学习方法通常无法实现。结果显示，同时优化参数准确性和不确定性校准并不会损害前者性能。此外，该模型具备检测实验数据漂移的能力，且推理速度显著快于基于似然和无似然的贝叶斯推断方法。这些结果表明，此类模型有望实现高精度、实时的参数估计并附带不确定性量化，是实验环境中部署的理想候选方案。
原文摘要 · Abstract (English)
We show that ensembles of deep neural networks, called deep ensembles, can be used to perform quantum parameter estimation while also providing a means for quantifying uncertainty in parameter estimates, which is a key advantage of using Bayesian inference for parameter estimation that is lost when using existing machine learning methods. We show that optimizing for both accurate parameter estimates and well calibrated uncertainty estimates does not lead to degradation in the former as opposed to only optimizing for accuracy. We also show that the drift detection capabilities of these ensemble models can be used to detect drift in the experimental data used during inference. This approach is also shown to provide much faster inference time than both likelihood-based and likelihood-free Bayesian inference. These results suggest that such models could enable accurate, real-time parameter estimation with quantified uncertainty, making them promising candidates for deployment in experimental settings.
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