Towards Faster Quantum Circuit Simulation Using Graph Decompositions, GNNs and Reinforcement Learning
Abstract
In this work, we train a graph neural network with reinforcement learning to more efficiently simulate quantum circuits using the ZX-calculus. Our experiments show a marked improvement in simulation efficiency using the trained model over existing methods that do not incorporate AI. In this way, we demonstrate a machine learning model that can reason effectively within a mathematical framework such that it enhances scientific research in the important domain of quantum computing.
Cite
Text
Koziell-Pipe et al. "Towards Faster Quantum Circuit Simulation Using Graph Decompositions, GNNs and Reinforcement Learning." NeurIPS 2024 Workshops: MATH-AI, 2024.Markdown
[Koziell-Pipe et al. "Towards Faster Quantum Circuit Simulation Using Graph Decompositions, GNNs and Reinforcement Learning." NeurIPS 2024 Workshops: MATH-AI, 2024.](https://mlanthology.org/neuripsw/2024/koziellpipe2024neuripsw-faster/)BibTeX
@inproceedings{koziellpipe2024neuripsw-faster,
title = {{Towards Faster Quantum Circuit Simulation Using Graph Decompositions, GNNs and Reinforcement Learning}},
author = {Koziell-Pipe, Alexander and Yeung, Richie and Sutcliffe, Matthew},
booktitle = {NeurIPS 2024 Workshops: MATH-AI},
year = {2024},
url = {https://mlanthology.org/neuripsw/2024/koziellpipe2024neuripsw-faster/}
}