Quantum circuit routing is a key step in compiling programs for noisy intermediate-scale quantum processors. Routes that appear efficient by standard overhead metrics can still lose fidelity when they pass through poorly calibrated couplers. We study a calibration-aware graph reinforcement-learning router that uses same-day IBM Heron r2 calibration data to choose hardware-edge SWAPs. We train the policy with proximal policy optimization and evaluate it with exact simulated fidelity across nine Munich Quantum Toolkit (MQT) Bench circuits and three calibration snapshots. Across these evaluations, pooled mean exact fidelity is $0.727$, compared with $0.440$ for SABRE-best20 and $0.481$ for target-aware SABRE. Fidelity gains come with higher routed two-qubit counts and are concentrated in the 5q and 8q circuit families; under the fixed tree action graph, all 10q families favor SABRE-best20. Overall, our results show that calibration-aware learned routing can improve fidelity beyond gate-count-driven compilation.

翻译：量子电路路由是将程序编译到含噪中等规模量子处理器上的关键步骤。由标准开销指标衡量为高效的路由方案，在通过标定较差的耦合器时仍可能导致保真度损失。我们研究了一种标定感知的图强化学习路由器，该路由器利用IBM Heron r2同日标定数据选择硬件边上的SWAP操作。采用近端策略优化训练策略，并通过九个慕尼黑量子工具包基准电路及三个标定快照的精确模拟保真度进行评估。在所有评估中，合并平均精确保真度为0.727，而SABRE-best20为0.440，目标感知SABRE为0.481。保真度增益伴随更高的路由双量子比特门数量，且主要集中在5量子比特和8量子比特电路族；在固定树形动作图下，所有10量子比特电路族更倾向SABRE-best20。总体而言，我们的结果表明，标定感知的学习路由可在门计数驱动编译之外提升保真度。