一句话：提出基于强化学习的量子线路优化器 Quarl，通过动作空间分解与图神经网络状态表示，在多数基准上显著优于现有优化器。
量子线路优化极具挑战：功能等价线路的搜索空间极其庞大，而且往往需要先施加暂时降低性能的变换，才能获得最终的性能提升。本文提出 Quarl——一个基于学习的量子线路优化器。将强化学习用于线路优化面临两大难题：动作空间大且随线路变化，以及状态表示不统一。Quarl 通过新颖的神经架构与 RL 训练流程应对这两个问题：它把动作空间分解为两部分，并在状态表示中引入图神经网络；其直觉是优化决策可主要由局部推理驱动，同时保留全局线路级推理能力。评估表明，Quarl 在几乎所有基准线路上都显著优于现有线路优化器；令人意外的是，Quarl 还能学会执行 rotation merging——这是一种复杂的非局部线路优化，在现有优化器中通常被实现为独立的 pass。
Optimizing quantum circuits is challenging due to the very large search space of functionally equivalent circuits and the necessity of applying transformations that temporarily decrease performance to achieve a final performance improvement. This paper presents Quarl, a learning-based quantum circuit optimizer. Applying reinforcement learning (RL) to quantum circuit optimization raises two main challenges: the large and varying action space and the non-uniform state representation. Quarl addresses these issues with a novel neural architecture and RL-training procedure. Our neural architecture decomposes the action space into two parts and leverages graph neural networks in its state representation, both of which are guided by the intuition that optimization decisions can be mostly guided by local reasoning while allowing global circuit-wide reasoning. Our evaluation shows that Quarl significantly outperforms existing circuit optimizers on almost all benchmark circuits. Surprisingly, Quarl can learn to perform rotation merging—a complex, non-local circuit optimization implemented as a separate pass in existing optimizers.
@article{paperbot2566,
title = {Quarl: A Learning-Based Quantum Circuit Optimizer},
author = {Zikun Li and Jinjun Peng and Yixuan Mei and Sina Lin and Yi Wu and Oded Padon and Zhihao Jia},
journal = {Proceedings of the ACM on Programming Languages},
volume = {8},
number = {OOPSLA1},
year = {2024},
doi = {10.1145/3649831}
}