Efficient Paramater Identification
Abstract
This project is related to basic research in the area of Quantum Control and System Identification. As quantum research is advancing from science to engineering, it is inevitably that some of the engineering doctrine in Control theory will play a significant role in managing quantum systems since Control theory is the foundation for many engineering problems. China has published papers in this area including applying Deep Machine Learning and Artificial Intelligence (AI) in quantum system engineering. Most of the researches in this area in the US are physics-based, instead of engineering-based approaches. This is a GAP in quantum researches between the US and China. To advance practical applications, it is vitally important to achieve high-precision identification and robust operations in quantum devices and systems. High-precision parameter identification and robust control of quantum systems are the fundamental tasks in quantum sensing, accurate manipulation, and reliable device fabrication. As the number of Qubits in quantum devices increases, the number of parameters to be identified, the amount of required physical resources, and the computational complexity grow exponentially. The widely used algorithms, such as maximum likelihood estimation and Bayesian mean estimation cannot be used to characterize such large systems. The unique approach of this project is to develop a recursive adaptive method that the current step is chosen based on the previous outcome. The recursive adaptive quantum state estimation can dramatically improve the speed and accuracy. The PI and his collaborators will develop the adaptive Bayesian protocol for quantum state tomography. The adaptive method is expected to reduce the total number of measurements required or estimation errors compared to non-adaptive methods. The project has three objectives. Object 1 is to develop adaptive identification algorithm to efficiently and accurately identify the Hamiltonian of quantum systems. Object 2 is to effectively determine the identification capability of spin-chain type of quantum sensors using similarity transformation approach (STA). Object 3 is to develop learning-based robust control algorithms with landscape analysis. Integration of Deep Machine Learning with the quantum optimal control theory can achieve reliable quantum operations in dynamic environments. Both PI and Co-PI are pioneers and well-known in this area for their many high-impact publications. I strongly recommend funding this project.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 30, 2019
- Source ID
- N629091912129
Entities
People
- Daoyi Dong
Organizations
- Office of Naval Research
- United States Navy
- University of New South Wales