ZX-diagrams are a powerful graphical language for the description of quantum
processes with applications in fundamental quantum mechanics, quantum circuit
optimization, tensor network simulation, and many more. The utility of
ZX-diagrams relies on a set of local transformation rules that can be applied
to them without changing the underlying quantum process they describe. These
rules can be exploited to optimize the structure of ZX-diagrams for a range of
applications. However, finding an optimal sequence of transformation rules is
generally an open problem. In this work, we bring together ZX-diagrams with
reinforcement learning, a machine learning technique designed to discover an
optimal sequence of actions in a decision-making problem and show that a
trained reinforcement learning agent can significantly outperform other
optimization techniques like a greedy strategy or simulated annealing. The use
of graph neural networks to encode the policy of the agent enables
generalization to diagrams much bigger than seen during the training phase.

ZX 图表是用于描述量子过程的强大图形语言，它在基础量子力学、量子电路优化、张量网络模拟等方面具有广泛应用。本研究将 ZX 图表与强化学习相结合，通过训练强化学习智能体，发现其在优化 ZX 图表结构方面表现优秀，超过了贪婪策略或模拟退火等其他优化技术。图形神经网络用于编码智能体的策略，实现了对训练阶段未见过的大规模图表的泛化。