Reinforcement Learning for Long-Distance Quantum Communication
Machine learning can help us in solving problems in the context of big-data analysis and classification, as well as in playing complex games such as Go. It can also be used to find novel protocols and algorithms for applications such as large-scale quantum communication. This paper shows that machine learning can be used to identify quantum communication protocols, including teleportation, entanglement purification, and the quantum repeater. The learning agent is provided with a universal gate set, and the desired task is specified via a reward scheme. From a technical perspective, the learning agent has to deal with stochastic environments and reactions. However, the usefulness of learning agents goes beyond the mere reproduction of known protocols; the same approach allows one to find improved solutions to long-distance communication problems. I will first introduce the reinforcement learning (RL). Then I will introduce some quantum teleportation protocol and how RL is applicated in finding quantum teleportation protocol.