Quarl: A Learning-Based Quantum Circuit Optimizer
This work addresses the problem of efficient quantum circuit optimization for researchers and practitioners in quantum computing, representing an incremental advancement with a novel learning-based approach.
The paper tackles the challenge of optimizing quantum circuits by introducing Quarl, a learning-based optimizer that uses reinforcement learning to outperform existing optimizers on most benchmark circuits, achieving significant performance improvements.
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.