Quantum compiling, a process that decomposes the quantum algorithm into a series of hardware-compatible commands or elementary gates, is of fundamental importance for quantum computing. We introduce an efficient algorithm based on deep reinforcement learning that compiles an arbitrary single-qubit gate into a sequence of elementary gates from a finite universal set. It generates near-optimal gate sequences with given accuracy and is generally applicable to various scenarios, independent of the hardware-feasible universal set and free from using ancillary qubits. For concreteness, we apply this algorithm to the case of topological compiling of Fibonacci anyons and obtain near-optimal braiding sequences for arbitrary single-qubit unitaries. Our algorithm may carry over to other challenging quantum discrete problems, thus opening up a new avenue for intriguing applications of deep learning in quantum physics.

中文翻译：

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强化学习的拓扑量子编译

量子编译是将量子算法分解为一系列硬件兼容命令或基本门的过程，对于量子计算至关重要。我们介绍了一种基于深度强化学习的高效算法，该算法将任意单量子位门限从有限的通用集编译为一系列基本门。它以给定的精度生成接近最佳的门序列，并且通常适用于各种情况，与硬件可行的通用集无关，并且无需使用辅助量子位。为了具体起见，我们将此算法应用于斐波那契任意子的拓扑编译情况，并为任意单量子位unit获得近似最优的编织序列。我们的算法可能会延续到其他具有挑战性的量子离散问题，