​The interaction with a slowly evolving environment is often considered the limiting factor for many spin qubits. However, in principle, the associated timescales extend far beyond a single qubit cycle, presenting an opportunity for the application of feedback control. In this presentation, I will:

• Explore the concept of actively controlled quantum computation,
• Showcase cutting-edge active control techniques applied to spin qubits in GaAs [1],
• Introduce a novel machine learning algorithm designed for adaptive Hamiltonian estimation [2],
• Provide new insights into the microscopic origins of low-frequency charge noise [3,4]

[1] F. Berritta, T. Rasmussen, J. A. Krzywda et al., arXiv:2308.02012 (2023)
[2] J. Benestad, J. A. Krzywda et al, arXiv:2309.15014 (2023)
[3] M. Kępa, Ł. Cywiński, J. A. Krzywda Appl. Phys. Lett. 123, 034005 (2023)
[4] M. Kępa, N. Focke, Ł. Cywiński, J. A. Krzywda Appl. Phys. Lett. 123, 034003 (2023)