Quantum-inspired learning algorithms with applications to quantum control
Quantum information processing is a rapidly developing field. Some results have shown that quantum computation can more efficiently solve some difficult problems than the classical counterpart. Some methods have also been explored to connect quantum computation and machine learning. For example, the quantum-computing version of artificial neural network has been studied from the pure theory to the simple simulated and experimental implementation. Quantum or quantum-inspired evolutionary algorithms have been proposed to improve the existing evolutionary algorithms. Taking advantage of quantum computation, some novel algorithms inspired by quantum characteristics will not only improve the performance of existing algorithms on traditional computers but also promote the development of related research areas such as quantum computers and machine learning. On the other hand, the development of quantum control theory has been recognised as one of key tasks for practical quantum technology. Learning control has been proven to be a potential design method for optimising control performance for complex quantum systems. Quantum-inspired learning algorithms may be especially suitable for specific quantum control tasks. The objective of this project is to develop new quantum-inspired learning algorithms and apply these algorithms to enhance control performance in the engineering of quantum systems. This project is in collaboration with Prof Tarn at Washington University (USA).
Description of Work:
- Develop new quantum-inspired learning algorithms (e.g., quantum-inspired PSO algorithm, quantum-inspired differential evolution algorithm) and test their learning performance by simulation
- Apply these new quantum-inspired learning algorithms to several typical quantum control problems and compare the control performance with existing design methods