Explainable AI for Quantum Circuit Optimization using Tensor Networks

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

Quantum circuit optimization is crucial for realizing the full potential of quantum computing, but current optimization techniques often operate as black boxes, lacking transparency and hindering trust. This paper proposes a novel approach combining tensor networks with explainable AI to address this challenge. We represent quantum circuits using tensor networks, allowing for a richer mathematical representation and facilitating the application of explainable AI methods. The developed system generates visualizations and textual explanations detailing the key factors driving optimization decisions, promoting understanding and trust in the process. The core claim highlights the critical need for explainability in AI-driven optimization, while the proposed mechanism leverages tensor networks to achieve this. We demonstrate the potential of this combined approach to enhance transparency and accelerate the adoption of quantum circuit optimization techniques. This work introduces a new paradigm for quantum optimization, moving beyond opaque algorithms towards interpretable and trustworthy solutions.

DOI: 10.5281/zenodo.22139273

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