Dataset: Parameter estimation by learning quantum correlations in continuous photon-counting data using neural networks

Dataset for the paper E. Rinaldi, M. González Lastre, S. García Herreros, S. Ahmed, M. Khanahmadi, F. Nori, and C. Sánchez Muñoz (2023), ¨Parameter estimation by learning quantum correlations in continuous photon-counting data using neural networks¨, arxiv: 2310.02309 This dataset can be used to populate the [datapath] folder in the repository <strong>ParamEst-NN</strong> (github.com/CarlosSMWolff/ParamEst-NN ) and reproduce the results shown in the paper. The dataset consist of four folders: <strong>Training trajectories.</strong> Records of quantum-jump trajectories simulated with the Monte-Carlo solver of the QuTiP library, used to train neural networks for the problem of quantum parameter estimation. The records consist of time delays between quantum jumps. <strong>Models. </strong>Models trained with the training trajectories provided, and used to obtain the results shown in the paper. <strong>Validation trajectories. </strong>Trajectories used to benchmark the trained models. For the 2D case, the same trajectories are provided as a single .npy file, and split in 10 separated batches inside a ```batches``` folder. These are the batches that we used to generate Bayesian estimations on a cluster using nested sampling (see README file of the repository). <strong>Cached results. </strong>Here we provide pre-computed Bayesian estimations for the 2D multi-parameter estimation case using nested sampling. <em>E.R. was supported by Nippon Telegraph and Telephone Corporation (NTT) Research during the early stages of this work.<br> C.S.M. acknowledges that the project that gave rise to these results received the support of a fellowship from “la Caixa” Foundation (ID 100010434) and from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No.847648, with fellowship code LCF/BQ/PI20/11760026, and financial support from the MCINN project PID2021-126964OB-I00 (QENIGMA) and the Proyecto Sinérgico CAM 2020 Y2020/TCS- 6545 (NanoQuCo-CM).</em>