Optimizing ZX-diagrams with deep reinforcement learning

Optimizing ZX-diagrams with deep reinforcement learning

Blog Article

ZX-diagrams are a powerful graphical language for the description of quantum processes with applications in Board Game 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 here techniques like a greedy strategy, simulated annealing, and state-of-the-art hand-crafted algorithms.

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.