Work

Optimizing ZX-diagrams with deep reinforcement learning

Year: 2024

Type: article

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.

Source: Machine Learning Science and Technology

DOI: 10.1088/2632-2153/ad76f7

Authors: Maximilian Nägele, Florian Marquardt

Institutions: Friedrich-Alexander-Universität Erlangen-Nürnberg, Max Planck Institute for the Science of Light

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,610

Funders: Max-Planck-Gesellschaft, Bayerisches Staatsministerium für Wissenschaft, Forschung und Kunst