[2509.10756] Quantum parameter estimation with uncertainty quantification from continuous measurement data using neural network ensembles

Quantum Physics arXiv:2509.10756 (quant-ph) [Submitted on 12 Sep 2025 (v1), last revised 1 Mar 2026 (this version, v2)] Title:Quantum parameter estimation with uncertainty quantification from continuous measurement data using neural network ensembles Authors:Amanuel Anteneh View a PDF of the paper titled Quantum parameter estimation with uncertainty quantification from continuous measurement data using neural network ensembles, by Amanuel Anteneh View PDF HTML (experimental) 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. 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:2509.10756v2 [qu...