Quantum parameter estimation with uncertainty quantification from continuous measurement data using neural network ensembles
This method uses ensembles of deep neural networks to estimate quantum parameters from continuous measurement data while simultaneously quantifying uncertainty. The approach outperforms traditional likelihood-based methods by optimizing for both accurate estimates and well-calibrated uncertainty, offering fast, real-time parameter estimation.