# Recap

In this video we review all the techniques we learned during the course in context of experimental tuning and general requirements for successful tuning algorithm.

# Main takeaways

- Fundamental difference between traditional programming and learning from data makes machine learning algorithms suitable for tuning
- Machine learning algorithms have an exciting potential for speed and generalization, but they still constitute a developing field.

# Further reading

Adversarial Hamiltonian learning for quantum dots in a minimal Kitaev chain: https://arxiv.org/abs/2304.10852

Contemporary example of how generative models for tuning/Hamiltonian learning are used in practice.

Advances in Automation of Quantum Dot Device Control: https://arxiv.org/abs/2112.09362

Very nice review paper about control and tuning of quantum dots.

Modern applications of machine learning in physical sciences: https://arxiv.org/abs/2204.04198

If you are interested in a more broad perspective on machine learning in quantum physics (beyond tuning) check out this forthcoming book.