Quarl: A Learning-Based Quantum Circuit Optimizer
Zikun Li Jinjun Peng Yixuan Mei Sina Lin Yi Wu Oded Padon Zhihao Jia
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.