Quarl: A Learning-Based Quantum Circuit Optimizer

TL;DR

Quarl is a novel learning-based quantum circuit optimizer that uses reinforcement learning and graph neural networks to effectively navigate large search spaces and outperform existing methods.

Contribution

Introduces Quarl, a reinforcement learning framework with a new neural architecture for quantum circuit optimization, capable of learning complex transformations like rotation merging.

Findings

Outperforms existing optimizers on benchmark circuits

Learns to perform complex, non-local optimizations like rotation merging

Uses graph neural networks for effective state representation

Abstract

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.