The general problem of quantum compiling is to approximate any unitary transformation that describes the quantum computation as a sequence of elements selected from a finite base of universal quantum gates. The Solovay-Kitaev theorem guarantees the existence of such an approximating sequence. Though, the solutions to the quantum compiling problem suffer from a tradeoff between the length of the sequences, the precompilation time, and the execution time. Traditional approaches are time-consuming, unsuitable to be employed during computation. Here, we propose a deep reinforcement learning method as an alternative strategy, which requires a single precompilation procedure to learn a general strategy to approximate single-qubit unitaries. We show that this approach reduces the overall execution time, improving the tradeoff between the length of the sequence and execution time, potentially allowing real-time operations.

量子编译的一般问题是近似任何将量子计算描述为从通用量子门的有限基中选择的元素序列的幺正变换。Solovay-Kitaev 定理保证了这样一个近似序列的存在。但是，量子编译问题的解决方案会在序列长度、预编译时间和执行时间之间进行权衡。传统方法耗时，不适合在计算过程中使用。在这里，我们提出了一种深度强化学习方法作为替代策略，它需要一个单一的预编译程序来学习近似单量子比特幺正的一般策略。我们表明这种方法减少了整体执行时间，