02428nas a2200217 4500008004100000245010600041210006900147260001400216520171900230653004301949653002701992653002902019653003102048100001702079700001502096700001302111700001702124700001602141700001602157856003702173 2022 eng d00aEfficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning0 aEfficient and practical quantum compiler towards multiqubit syst c4/14/20223 a
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, and AQ* search. In this way, our protocol is compatible with various quantum machines and can be used to compile multi-qubit operators. We systematically evaluate the performance of our proposal in compiling quantum operators with both inverse-closed and inverse-free universal basis sets. In the task of single-qubit operator compiling, our proposal outperforms other RL-based quantum compilers in the measure of compiling sequence length and inference time. Meanwhile, the output solution is near-optimal, guaranteed by the Solovay-Kitaev theorem. Notably, for the inverse-free universal basis set, the achieved sequence length complexity is comparable with the inverse-based setting and dramatically advances previous methods. These empirical results contribute to improving the inverse-free Solovay-Kitaev theorem. In addition, for the first time, we demonstrate how to leverage RL-based quantum compilers to accomplish two-qubit operator compiling. The achieved results open an avenue for integrating RL with quantum compiling to unify efficiency and practicality and thus facilitate the exploration of quantum advantages.
10aFOS: Computer and information sciences10aFOS: Physical sciences10aMachine Learning (cs.LG)10aQuantum Physics (quant-ph)1 aChen, Qiuhao1 aDu, Yuxuan1 aZhao, Qi1 aJiao, Yuling1 aLu, Xiliang1 aWu, Xingyao uhttps://arxiv.org/abs/2204.06904