Learning control and performance optimisation of complex quantum systems

Program Code: 

Dr Daoyi Dong (d.dong@adfa.edu.au)

Description of Work: 


Quantum Technology has been recognised as one of the most promising frontier technologies. Although great progress has already been made, a lot of fundamental research is still needed for this area to become mature enough to foster wider practical applications. Much research in this area can be formulated as quantum control problems. Quantum control theory is drawing wide attention with research in this regard involving controllability, optimal control, feedback control, etc. A challenging task is the development of new theories and algorithms for the control analysis and synthesis of complex quantum systems. Learning control has been proven to be a potential design method for optimising control performance for complex quantum systems. The objective of this project is to develop new learning algorithms to enhance control performance in the engineering of complex quantum systems. This project is in collaboration with Prof Rabitz at Princeton University (USA).

Description of Work:

  • Formulate a collection of practical tasks arising in the quantum domain as control problems for complex quantum systems with a well-defined performance index.
  • Develop new learning algorithms to achieve improved control performance for complex quantum systems.