基于深度强化学习的原子硅量子点电路自动设计
Automated Atomic Silicon Quantum Dot Circuit Design via Deep Reinforcement Learning
摘要：
强大的自动化设计工具对于任何计算技术的扩散都至关重要。我们介绍了硅悬挂键量子点计算技术的第一个自动化设计工具，这是一个非常通用和灵活的单原子计算电路框架。自动化设计器能够通过采用tabula rasa双深度Q学习强化学习算法来导航任意大小的设计区域和真值表的复杂的多维设计空间。鲁棒的政策收敛证明了广泛的两个输入，一个输出逻辑电路和两个输入，两个输出半加法器，设计了一个数量级少的SiDBs在几个数量级的时间比文献中唯一的其他半加法器。我们预计，基于强化学习的自动化设计工具将加速SiDB量子点计算技术的发展，最终将其应用于专业计算硬件。
英文摘要：
Robust automated design tools are crucial for the proliferation of any computing technology. We introduce the first automated design tool for the silicon dangling bond quantum dot computing technology, which is an extremely versatile and flexible single-atom computing circuitry framework. The automated designer is capable of navigating the complex, hyperdimensional design spaces of arbitrarily sized design areas and truth tables by employing a tabula rasa double-deep Q-learning reinforcement learning algorithm. Robust policy convergence is demonstrated for a wide range of two-input, one-output logic circuits and a two-input, two-output half-adder, designed with an order of magnitude fewer SiDBs in several orders of magnitude less time than the only other half-adder demonstrated in the literature. We anticipate that reinforcement learning-based automated design tools will accelerate the development of the SiDB quantum dot computing technology, leading to its eventual adoption in specialized computing hardware.
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