Quantum Circuit Verification via Graph Neural Networks

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

The verification of quantum circuits is a critical step in ensuring the reliable execution of quantum computations. Traditional methods often struggle with the exponential complexity arising from the superposition and entanglement inherent in quantum mechanics. This work proposes a novel approach utilizing Graph Neural Networks (GNNs) to address this challenge. Quantum circuits are represented as graphs, where nodes correspond to quantum gates and edges represent data flow. The GNN learns to identify patterns within these graph representations that signify potential errors or deviations from the intended circuit behavior. This automated verification process offers a scalable solution compared to exhaustive simulation or analytical methods. The core of this approach lies in leveraging the expressive power of GNNs to capture complex correlations within the circuit structure, ultimately leading to more efficient and accurate verification. We demonstrate the feasibility and effectiveness of this methodology through a theoretical framework and outline future research directions.

DOI: 10.5281/zenodo.22219957

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