Research on quantum satellite access selection based on Q-learning quantum ant colony algorithm

The development and deployment of low-orbit quantum satellites has become one of the important research directions in the field of quantum communication technology. In order to improve the security and service quality of quantum satellite network, this paper proposes a multi-attribute quantum satellite selection access technique based on Q-learning quantum ant colony. Considering the two optimization objectives of path combined trust value and path cost, the overall trust function is defined according to the direct trust value and indirect trust value to improve the security, and the cost function is defined according to the channel QBER(Quantum bit error rate), interstellar transmission delay, and channel establishment rate to improve the service quality. Simulation results show that compared with the traditional ant colony optimization algorithm and the improved quantum ant colony multi-constraint algorithm, the Q-learning quantum ant colony algorithm significantly improves the performance indexes such as the packet delivery rate and the system blocking rate of the quantum satellite access selection, and improves the convergence speed, which is of certain reference significance for the research on the access selection of quantum satellites.