Towards Faster Quantum Circuit Simulation Using Graph Decompositions, GNNs and Reinforcement Learning

Alexander Koziell-Pipe, Richie Yeung, Matthew Sutcliffe

Keywords: quantum computing, graph neural network, reinforcement learning, zx calculus

TL;DR: We train a graph neural network with reinforcement learning to leverage an abstract mathematical framework to simulate quantum circuits more efficiently.

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.

Concurrent Submissions: N/A

Submission Number: 55