Efficient quantum compiling is essential for complex quantum algorithms realization. The Solovay–Kitaev (S–K) theorem offers a theoretical lower bound on the required operations for approaching any unitary operator. However, it is still an open question that this lower bound can be actually reached in practice. Here, we present an efficient quantum compiler which, for the first time, approaches the S–K lower bound in practical implementations, both for single-qubit and two-qubit scenarios, marking a significant milestone. Our compiler leverages deep reinforcement learning (RL) techniques to address current limitations in terms of optimality and inference time. Furthermore, we show that our compiler is versatile by demonstrating comparable performance between inverse-free basis sets, which is always the case in real quantum devices, and inverse-closed sets. Our findings also emphasize the often-neglected constant term in scaling laws, bridging the gap between theory and practice in quantum compiling. These results highlight the potential of RL-based quantum compilers, offering efficiency and practicality while contributing novel insights to quantum compiling theory.

中文翻译：

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针对具有深度强化学习的多量子位系统的高效实用的量子编译器 * * 作者列表按字母顺序排列。


高效的量子编译对于实现复杂的量子算法至关重要。索洛瓦伊-基塔耶夫 (S-K) 定理为逼近任何酉算子所需的运算提供了理论上的下界。然而，在实践中能否真正达到这个下限仍然是一个悬而未决的问题。在这里，我们提出了一种高效的量子编译器，它首次在单量子位和双量子位场景的实际实现中接近 S-K 下界，标志着一个重要的里程碑。我们的编译器利用深度强化学习 (RL) 技术来解决当前在最优性和推理时间方面的限制。此外，我们通过展示无逆基组（真实量子设备中始终如此）和逆闭集之间的可比较性能来证明我们的编译器是通用的。我们的研究结果还强调了缩放定律中经常被忽视的常数项，弥合了量子编译理论与实践之间的差距。这些结果凸显了基于强化学习的量子编译器的潜力，提供效率和实用性，同时为量子编译理论提供新颖的见解。