用强化学习自动组合量子电路优化步骤，效果优于人工设计的默认流程。
Reinforcement Learning for Adaptive Composition of Quantum Circuit Optimisation Passes
- 训练强化学习智能体从预设优化步骤中自适应选择序列。
- 在测试集上平均减少57.7%、中位数减少56.7%的双量子比特门。
- 适合需要高效优化量子电路的研究者与工具开发者。
许多量子软件开发套件提供了多组电路优化步骤。这些步骤已在孤立情况下高度优化并经过验证，但其应用顺序通常由用户决定，或采用通用默认序列。然而，通用序列会遗漏特定电路的优化机会；而为个别电路定制序列则需要深厚的量子电路设计与优化知识。本文提出并演示了训练一个强化学习智能体来自动组合优化步骤。具体而言，该智能体的动作空间包含用于减少双量子比特门数量的优化步骤，这些步骤源自PyTKET的默认序列。在多样化的测试电路中，智能体所生成序列的（平均值，中位数）双量子比特门减少比例分别为57.7%和56.7%，显著优于次优的默认序列（41.8%和50.0%）。
原文摘要 · Abstract (English)
Many quantum software development kits provide a suite of circuit optimisation passes. These passes have been highly optimised and tested in isolation. However, the order in which they are applied is left to the user, or else defined in general-purpose default pass sequences. While general-purpose sequences miss opportunities for optimisation which are particular to individual circuits, designing pass sequences bespoke to particular circuits requires exceptional knowledge about quantum circuit design and optimisation. Here we propose and demonstrate training a reinforcement learning agent to compose optimisation-pass sequences. In particular the agent's action space consists of passes for two-qubit gate count reduction used in default PyTKET pass sequences. For the circuits in our diverse test set, the (mean, median) fraction of two-qubit gates removed by the agent is $(57.7\%, \ 56.7 \%)$, compared to $(41.8 \%, \ 50.0 \%)$ for the next best default pass sequence.
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