用强化学习优化含任意Pauli旋转的量子电路，显著减少门数和深度。
Pauli Network Circuit Synthesis with Reinforcement Learning
- 通过学习启发式策略分块合成电路，兼顾硬件连接约束。
- 6比特随机电路下两量子门数减少超2倍，单次耗时不足10毫秒。
- 可嵌入Qiskit编译器，平均降低20%门数与深度，部分达60%。
我们提出一种基于强化学习（RL）的方法，用于包含任意Pauli旋转与Clifford操作的量子电路重综合。通过将每个子模块压缩为紧凑表示，并逐步采用学习到的启发式策略进行合成，得到的电路更短且符合硬件连接约束。在6量子比特随机Pauli网络上直接对比当前最先进的启发式方法，该RL方法实现两量子门数量超过2倍的减少，单个电路执行时间低于10毫秒。进一步将该方法集成至收集-重综合流水线中，作为Qiskit编译器的优化步骤，在Benchpress基准测试中观察到两量子门数量与深度平均降低20%，部分实例最高达60%。这些结果表明，基于强化学习的合成在真实大规模量子编译任务中具有显著提升电路质量的潜力。
原文摘要 · Abstract (English)
We introduce a Reinforcement Learning (RL)-based method for re-synthesis of quantum circuits containing arbitrary Pauli rotations alongside Clifford operations. By collapsing each sub-block to a compact representation and then synthesizing it step-by-step through a learned heuristic, we obtain circuits that are both shorter and compliant with hardware connectivity constraints. We find that the method is fast enough and good enough to work as an optimization procedure: in direct comparisons on 6-qubit random Pauli Networks against state-of-the-art heuristic methods, our RL approach yields over 2x reduction in two-qubit gate count, while executing in under 10 milliseconds per circuit. We further integrate the method into a collect-and-re-synthesize pipeline, applied as a Qiskit transpiler pass, where we observe average improvements of 20% in two-qubit gate count and depth, reaching up to 60% for many instances, across the Benchpress benchmark. These results highlight the potential of RL-driven synthesis to significantly improve circuit quality in realistic, large-scale quantum transpilation workloads.
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