Mathematica code for analytical results in "Supervised learning of time-independent Hamiltonians for gate design"

  • Luca Innocenti (Creator)

Dataset

Description

Mathematica code used to reproduce the analytical results presented in "Supervised learning of time-independent Hamiltonians for gate design"
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
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    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

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    Cite this

    Innocenti, L. (Creator) (Aug 2020). Mathematica code for analytical results in "Supervised learning of time-independent Hamiltonians for gate design". Queen's University Belfast. 10.17034/0b94d99f-0417-4a3d-98b9-2a20ab89c132