Shortening quantum circuits is crucial to reducing the destructive effect of environmental decoherence and enabling useful algorithms. Here, we demonstrate an improvement in such compilation tasks via a combination of using hybrid discrete-continuous optimization across a continuous gate set, and architecture-tailored implementation. The continuous parameters are discovered with a gradient-based optimization algorithm, while in tandem the optimal gate orderings are learned via a deep reinforcement learning algorithm, based on projective simulation. To test this approach, we introduce a framework to simulate collective gates in trapped-ion systems efficiently on a classical device. The algorithm proves able to significantly reduce the size of relevant quantum circuits for trapped-ion computing. Furthermore, we show that our framework can also be applied to an experimental setup whose goal is to reproduce an unknown unitary process.

中文翻译：

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具有深度强化学习的俘获离子量子电路的混合离散连续编译

缩短量子电路对于减少环境退相干的破坏性影响并实现有用的算法至关重要。在这里，我们通过跨连续门集使用混合离散连续优化和架构定制实现相结合，展示了此类编译任务的改进。使用基于梯度的优化算法发现连续参数，同时通过基于投影模拟的深度强化学习算法学习最佳门排序。为了测试这种方法，我们引入了一个框架，可以在经典设备上有效地模拟捕获离子系统中的集体门。该算法被证明能够显着减小用于俘获离子计算的相关量子电路的尺寸。此外，我们表明我们的框架也可以应用于实验设置，其目标是重现未知的单一过程。