Control of five-qubit system based on quantum reinforcement learning

Dong, Daoyi; Chen, Zonghai; Chen, Chunlin; Zhang, Chenbin

Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

Control of five-qubit system based on quantum reinforcement learning

Date

2006

Authors

Abstract

Controlling the multi-qubit system is a key task for practical quantum information processing. In this paper, the control problem of five-qubit is studied. A novel quantum reinforcement learning algorithm based on quantum superposition principle is proposed for the quantum control problem. The simulated result shows that quantum reinforcement learning can effectively find the optimal control sequence through fast learning.