Research output per year
Research output per year
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.
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 available | Aug 2020 |
|---|---|
| Publisher | Queen's University Belfast |
| Date of data production | 2020 - |
Research output
- 1 Article
-
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 journal › Article › peer-review
Open AccessFile18 Citations (Scopus)228 Downloads (Pure)
Student theses
-
Machine-learning-assisted state and gate engineering for quantum technologies
Innocenti, L. (Author), Ferraro, A. (Supervisor) & Paternostro, M. (Supervisor), Dec 2020Student thesis: Doctoral Thesis › Doctor of Philosophy
File