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MON: Monday Contributed Sessions
MON 18: Quantum Algorithms
MON 18.5: Vortrag
Montag, 8. September 2025, 17:30–17:45, ZHG007
Optimizing ZX-diagrams with deep reinforcement learning — •Maximilian Nägele1,2 and Florian Marquardt1,2 — 1Max Planck Institute for the Science of Light, Staudtstraße 2, 91058 Erlangen, Germany — 2Physics Department, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
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, 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.
Keywords: ZX-diagrams; reinforcement learning; quantum computing