Optimizing ZX-Diagrams with Deep Reinforcement Learning

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 appl...

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Bibliographic Details
Published in:arXiv.org 2024-09
Main Authors: Nägele, Maximilian, Marquardt, Florian
Format: Article
Language:English
Subjects:
Circuits
Deep learning
Graph neural networks
Machine learning
Optimization
Optimization techniques
Quantum mechanics
Simulated annealing
Tensors
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2311.18588