  One of the ambitious goals of artificial intelligence is to build a machine
that outperforms human intelligence, even if limited knowledge and data are
provided. Reinforcement Learning (RL) provides one such possibility to reach
this goal. In this work, we consider a specific task from quantum physics, i.e.
quantum state transfer in a one-dimensional spin chain. The mission for the
machine is to find transfer schemes with fastest speeds while maintaining high
transfer fidelities. The first scenario we consider is when the Hamiltonian is
time-independent. We update the coupling strength by minimizing a loss function
dependent on both the fidelity and the speed. Compared with a scheme proven to
be at the quantum speed limit for the perfect state transfer, the scheme
provided by RL is faster while maintaining the infidelity below $5\times
10^{-4}$. In the second scenario where a time-dependent external field is
introduced, we convert the state transfer process into a Markov decision
process that can be understood by the machine. We solve it with the deep
Q-learning algorithm. After training, the machine successfully finds transfer
schemes with high fidelities and speeds, which are faster than previously known
ones. These results show that Reinforcement Learning can be a powerful tool for
quantum control problems.
