Dataset for "Supervised learning of time-independent Hamiltonians for gate design"

  • Luca Innocenti (Creator)

Dataset

Description

Code and notebooks used to produce the results presented in:

Luca Innocenti, Leonardo Banchi, Alessandro Ferraro, Sougato Bose, Mauro Paternostro (2020). "Supervised learning of time-independent Hamiltonians for gate design". New J. Phys. 22 065001 (arXiv:1803.07119).

Content:
Notebooks: Usage examples in the form of jupyter notebooks.
Src: Source code.
Data: Data used to produce the results shown in the paper, and more. It mostly contains saved trained nets, and its content is intended to be used through the provided interfaces (mostly defined in net_analysis_tools.py), as shown in the example notebooks.
Date made availableAug 2020
PublisherQueen's University Belfast
Date of data production2020 -

Research Output

Supervised learning of time-independent Hamiltonians for gate design

Innocenti, L., Banchi, L., Ferraro, A., Bose, S. & Paternostro, M., 17 Jun 2020, In : New Journal of Physics. 22, 24 p., 065001.

Research output: Contribution to journalArticle

Open Access
File
  • 80 Downloads (Pure)

    Student Theses

    Machine-learning-assisted state and gate engineering for quantum technologies

    Author: Innocenti, L., Dec 2020

    Supervisor: Ferraro, A. (Supervisor) & Paternostro, M. (Supervisor)

    Student thesis: Doctoral ThesisDoctor of Philosophy

    File

    Cite this

    Innocenti, L. (Creator) (Aug 2020). Dataset for "Supervised learning of time-independent Hamiltonians for gate design". Queen's University Belfast. 10.17034/897cb9ec-de7f-4a5a-a787-45700b1fa54d