Optimizing ZX-diagrams with deep reinforcement learning
Nagele M, Marquardt F (2024)
Publication Type: Journal article
Publication year: 2024
Journal
Book Volume: 5
Journal Issue: 3
Abstract
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.
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Germany (DE)How to cite
APA:
Nagele, M., & Marquardt, F. (2024). Optimizing ZX-diagrams with deep reinforcement learning. Machine Learning: Science and Technology, 5(3). https://doi.org/10.1088/2632-2153/ad76f7
MLA:
Nagele, Maximilian, and Florian Marquardt. "Optimizing ZX-diagrams with deep reinforcement learning." Machine Learning: Science and Technology 5.3 (2024).
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