Dataset: Parameter estimation by learning quantum correlations in continuous photon-counting data using neural networks
Dataset:Parameter estimation by learning quantum correlations in continuous photon-counting data using neural networks
DOI10.5281/zenodo.8305509Zenodo8305509MaRDI QIDQ6702687
Dataset published at Zenodo repository.
Author name not available (Why is that?)
Publication date: 3 October 2023
Copyright license: No records found.
Dataset for the paper E. Rinaldi, M.Gonzlez Lastre, S. Garca Herreros, S.Ahmed, M.Khanahmadi, F. Nori, and C. Snchez Muoz (2023), Parameter estimation by learning quantum correlations in continuousphoton-counting data using neural networks,arxiv: 2310.02309 This dataset can be used to populate the [datapath] folder in the repository ParamEst-NN (github.com/CarlosSMWolff/ParamEst-NN) and reproduce the results shown in the paper. The dataset consist of four folders: Training trajectories.Records of quantum-jump trajectories simulated with the Monte-Carlo solver of the QuTiPlibrary, used to train neural networks for the problem of quantum parameter estimation. The records consist of time delays between quantum jumps. Models.Models trained with the training trajectories provided, and used to obtain the results shown in the paper. Validation trajectories.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). Cached results.Here we provide pre-computed Bayesian estimations for the 2D multi-parameter estimation case using nested sampling. E.R. was supported by Nippon Telegraph and Telephone Corporation (NTT) Research during the early stages of this work. 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 Unions 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 Sinrgico CAM 2020 Y2020/TCS- 6545 (NanoQuCo-CM).
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