Quarl: A Learning-Based Quantum Circuit Optimizer

• URL: http://arxiv.org/abs/2307.10120v1
• Date: Mon, 17 Jul 2023 19:21:22 GMT
• Title: Quarl: A Learning-Based Quantum Circuit Optimizer
• Authors: Zikun Li, Jinjun Peng, Yixuan Mei, Sina Lin, Yi Wu, Oded Padon, Zhihao Jia
• Abstract summary: This paper presents Quarl, a learning-based quantum circuit. Applying reinforcement learning to quantum circuit optimization raises two main challenges: the large and varying action space and the non-uniform state representation.
• Score: 8.994999903946848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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