Machine learning for estimation and control of quantum systems

The advancement of quantum technologies depends on the ability to create and manipulate increasingly complex quantum systems, with critical applications in quantum computation, quantum simulation and quantum sensing. These developments present substantial challenges in efficient control, calibration and verification of quantum systems. Machine learning methods have emerged as powerful tools owing to their remarkable capability to learn from data, and have thus been extensively utilized for various quantum tasks. This paper reviews several significant topics at the intersection of machine learning and quantum estimation and control. Specifically, we discuss neural network-based approaches for quantum state estimation, gradient-based methods for quantum optimal control, evolutionary computation for learning control of quantum systems, machine learning techniques for quantum robust control and reinforcement learning for adaptive quantum control.