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This chapter presents a comprehensive study on integrating Explainable Artificial Intelligence (XAI) with Quantum Machine Learning (QML) for addressing key environmental challenges such as climate modelling, air quality prediction, resource optimization and disaster forecasting. A unified Explainable Quantum Machine Learning (EQML) framework is proposed, designed to ensure transparency and interpretability across environmental applications. The framework combines quantum neural networks, quantum support vector machines and quantum optimizers with model-agnostic explainability techniques such as SHAP, LIME and Q-LIME. Case studies demonstrate that while quantum models currently offer comparable performance to classical baselines under Noisy Intermediate-Scale Quantum (NISQ) conditions, their explainability reveals deeper insights into data relationships and domain causality. The results highlight that integrating explainability into quantum systems builds user trust, supports scientific validation and lays the foundation for transparent quantum-enabled sustainability solutions.

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

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  • 发表日期：

    2026年

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  • P. Jeyalakshmi

摘要

This chapter presents a comprehensive study on integrating Explainable Artificial Intelligence (XAI) with Quantum Machine Learning (QML) for addressing key environmental challenges such as climate modelling, air quality prediction, resource optimization and disaster forecasting. A unified Explainable Quantum Machine Learning (EQML) framework is proposed, designed to ensure transparency and interpretability across environmental applications. The framework combines quantum neural networks, quantum support vector machines and quantum optimizers with model-agnostic explainability techniques such as SHAP, LIME and Q-LIME. Case studies demonstrate that while quantum models currently offer comparable performance to classical baselines under Noisy Intermediate-Scale Quantum (NISQ) conditions, their explainability reveals deeper insights into data relationships and domain causality. The results highlight that integrating explainability into quantum systems builds user trust, supports scientific validation and lays the foundation for transparent quantum-enabled sustainability solutions.