Najbliższe seminaria

27 VAdith Sai Aramthottil (Jagiellonian University)
Phenomenology of many-body localization in bond-disordered spin chains
Gdzie: MS Teams [ZOA-test], 12:15
Seminarium Zakładowe
Online: [link]
Pokaż pozostałe

[Abstrakt, pełna informacja]

20-22.02.2024 - Workshop on Quantum Simulators of the Future: From Dynamical Gauge Fields to Lattice Gauge Theories | (smr 3922)

An ICTP Meeting This Workshop will gather world-leading groups that design, realize, and characterize a new generation of simulators with ultracold atoms and beyond. It will address novel quantum simulators of statistical gauge fields, dynamical lattices, and lattice gauge theory models (LGT), as well as connections to quantum computing and tensor network methods.


06-08.09.2023 - Konferencja "Time Crystals"

More details na stronie konferencji.

27.06-2.07.2022 - 6th Workshop on Algebraic Designs, Hadamard Matrices & Quanta

Więcej szczegółów on conference website.

05-11.09.2021 - Quantum Optics X

Więcej szczegółów na stronie konferencji.

Open Data

Current: /2303.13940

Arxiv link

For the code, see

Notebooks with examples:

  1. Dataset and explainability readout Open In Colab
  2. Simple training and evaluation Open In Colab
  3. Performance analysis for all models shown in the paper Open In Colab
  4. Focal averaging example Open In Colab


This database contains:

  1. 2-dimensional photoelectron momentum distributions resulting from ionization of Argon target by femtosecond laser pulses:
    1. data_raw/ - results of the simulations and experiments that are in their original format. Details about the data are in the README files.
    2. data_preprocessed/ - data from data_raw that has a uniform numpy format (same momentum grid) with labels and can be used directly for the machine learning part.
  2. models/
    1. models.tar.gz - all 400 pretrained deep convolutional neural network regression models trained in this project (very large)
    2. QProp_Ar_small_sample.npz_SL_0.0__BayesianVGG16__1000.h5 - one example of a model trained on a small dataset, used in the jupyter notebook example 02_training.ipynb
  3. analysis_all_model.pkl - python pickle with benchmarks of all trained models. Used in the 03_evaluation_all_models.ipynb notebook.
  4. explainability.npz - numpy archive with numerical results of the explainability part of the paper. It is used by the 01_dataset_readout.ipynb notebook.


If you use the repository, please cite the paper:

      title={Femtosecond pulse parameter estimation from photoelectron momenta using machine learning},
      author={Tomasz Szołdra and Marcelo F. Ciappina and Nicholas Werby and Philip H. Bucksbaum and Maciej Lewenstein and Jakub Zakrzewski and Andrew S. Maxwell},


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