1. Supervised learning with feed-forward networks:  Notebook 1

2. Supervised learning with convolutional neural networks:  Notebook 2

3. Ultracold atoms Hamiltonian learning examples: Notebook 3, Notebook 4

1. Quantum dot tuning exercises:   QD Tuning 1, QD Tuning 2

2. PCA + more unsupervised learning examples:  Unsupervised Learning Notebook

1. Unsupervised learning of topological phases:
   Eliska Greplova, Agnes Valenti, Gregor Boschung, Frank Schäfer, Niels Lörch, Sebastian Huber: Unsupervised identification of topological order using predictive models, New J. Phys. 22 045003 (2020)
   [arXiv: 1910.10124][code]
   
   

2. Tuning of quantum dots:
   Renato Durrer, Benedikt Kratochwil, Jonne V. Koski, Andreas J. Landig, Christian Reichl, Werner Wegscheider, Thomas Ihn, Eliska Greplova: Automated tuning of double quantum dots into specific charge states using neural networks, Phys. Rev. Applied 13, 054019 (2020), editors’ suggestion
   [arXiv: 1912.02777][code]
   
   

3. Hamiltonian learning for cold atoms:
   Agnes Valenti, Guliuxin Jin, Julian Leonard, Sebastian Huber, Eliska Greplova: Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum dynamics, Phys. Rev. Research 4, L012010 (2022)
   [arXiv:2103.01240][code]
   
   

4. Conditional GANs Hamiltonian learning
   Rouven Koch, David van Driel, Alberto Bordin, Jose L. Lado, Eliska Greplova: Adversarial Hamiltonian learning of quantum dots in a minimal Kitaev chain, Phys. Rev. Applied 20, 044081 (2023).
   [arXiv: 2304.10852][code]
   
   

If you want to learn more, check out our full publication list here.