Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning. (arXiv:2204.06904v1 [quant-ph])

Efficient quantum compiling tactics greatly enhance the capability of quantum
computers to execute complicated quantum algorithms. Due to its fundamental
importance, a plethora of quantum compilers has been designed in past years.
However, there are several caveats to current protocols, which are low
optimality, high inference time, limited scalability, and lack of universality.
To compensate for these defects, here we devise an efficient and practical
quantum compiler assisted by advanced deep reinforcement learning (RL)
techniques, i.e., data generation, deep Q-learning, …

arxiv learning quantum qubit reinforcement reinforcement learning systems