arXiv：cs.LG· Amanuel Anteneh·· 5 小时前AI 评分32
深度集成神经网络实现量子参数估计与不确定性量化
Quantum parameter estimation with uncertainty quantification from continuous measurement data using neural network ensembles
AI 导读
研究表明，深度集成（deep ensembles）神经网络可在量子参数估计中同时给出参数估计值与不确定性量化，且兼顾两者不会降低估计精度。该方法推理速度远快于基于似然和无似然的贝叶斯推断，并能通过漂移检测发现推理阶段实验数据的漂移，有望用于实验场景的实时参数估计。
正文
Abstract: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.
| Subjects: | Quantum Physics (quant-ph); Machine Learning (cs.LG) |
| Cite as: | arXiv:2509.10756 [quant-ph] |
| (or arXiv:2509.10756v4 [quant-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2509.10756 arXiv-issued DOI via DataCite |
Submission history
From: Amanuel Anteneh [view email]
[v1]
Fri, 12 Sep 2025 23:58:44 UTC (3,712 KB)
[v2]
Sun, 1 Mar 2026 19:58:26 UTC (3,931 KB)
[v3]
Fri, 6 Mar 2026 00:53:07 UTC (4,456 KB)
[v4]
Thu, 1 Oct 2026 19:07:16 UTC (5,273 KB)
来源：arXiv：cs.LG · arxiv.org