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.

翻译：ZX图是一种强大的图形化语言，用于描述量子过程，其应用涵盖基础量子力学、量子电路优化、张量网络模拟等诸多领域。ZX图的实用性依赖于一组局部变换规则，这些规则可在不改变其描述的底层量子过程前提下对其施加。通过利用这些规则，可针对各类应用优化ZX图的结构。然而，寻找最优的变换规则序列通常是一个开放性问题。在本工作中，我们将ZX图与强化学习这一旨在决策问题中发现最优行动序列的机器学习技术相结合，并证明经过训练的强化学习智能体能够显著优于贪心策略或模拟退火等其他优化技术。采用图神经网络编码智能体的策略，使得模型能够泛化至训练阶段未见过的更大规模图结构。