Explainable AI for Environmental Solutions: Building Trust and Transparency in Quantum Systems

Shashank Sheshar Singh, Sumit Kumar, Rohit Ahuja +1 more

Davinder Kaur, Süleyman Uslu, Arjan Durresi A

Jincheng Zhang

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.

Avik Mahata

Jincheng Zhang

The increasing deployment of Artificial Intelligence (AI) systems across various domains necessitates a deeper understanding of their decision-making processes. Current explainable AI (XAI) techniques often struggle to provide truly transparent and reliable explanations, particularly for complex models like deep neural networks. This paper proposes a novel approach to algorithmic transparency and XAI leveraging concepts from quantum information theory. Specifically, we explore the potential of quantum entanglement and mutual information to represent and analyze AI model decisions in a fundamentally different way. Our framework represents model outputs as quantum states and investigates the correlations between these states and the input data. This analysis aims to identify the key factors driving the model's decision and provide a more intuitive and understandable explanation. We demonstrate that utilizing quantum information theory can offer a richer and more robust approach to XAI than traditional methods, potentially addressing limitations related to interpretability and trust in AI systems. This work lays the groundwork for a new paradigm in AI transparency, utilizing the unique properties of quantum mechanics to enhance the explainability of complex algorithms.