Parameter estimation with uncertainty quantification from continuous measurement data using neural network ensembles
This work addresses uncertainty quantification in quantum parameter estimation for researchers, offering a more data-efficient and noise-robust alternative to Bayesian methods, though it appears incremental as it builds on existing ensemble techniques.
The paper tackled quantum parameter estimation by using deep neural network ensembles to achieve robust performance against noise and reduce data requirements compared to Bayesian inference, with results showing comparable performance to methods that reach the ultimate precision limit.
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. These models are shown to be more robust to noise in the measurement results used to perform the parameter estimation as well as noise in the data used to train them. We also show that much less data is needed to achieve comparable performance to Bayesian inference based estimation, which is known to reach the ultimate precision limit as more data is collected, than was used in previous proposals.