Online Seminar: Pan Pallittapongarnpim, Chulalongkorn U.

Machine Learning for Adaptive Phase Estimation

SPEAKER: Pan Pallittapongarnpim

AFFILIATION: Chula Intelligent and Complex Systems, Chulalongkorn University, Bangkok, Thailand

HOSTED BY: Dr Márika Kieferová, UTS Centre for Quantum Software and Information

ABSTRACT:
Quantum control steers the dynamic of a quantum system to the desired outcome typically through the guidance of a mathematical model. However, using a model to design control procedures becomes challenging when the knowledge of the system of incomplete or the system becomes too large or too complex to be captured usefully in systems of equations. In this talk, I will present how a known framework in control engineering, namely data-driven control, can be formulated for quantum control, enabling us to devise control procedures without the need of a dynamic model and connecting machine learning techniques to quantum control problems. I will then talk about an example of a quantum control problem that data-driven quantum control has been applied, i.e., adaptive quantum-enhanced phase estimation, whose adaptive control procedure is challenging to design due to the complexity of the quantum system’s response to measurement. In particular, I will discuss the case where phase noise is present but is not known to the control supervisor or the controller, emulating the case where the knowledge of the quantum dynamic is incomplete.