Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning

Explainability is becoming critical for quantum machine learning: As quantum computing moves from research labs to cloud-based services available to broader users, understanding why quantum circuits make specific decisions becomes as important as their accuracy—similar to how explainability has become essential for classical AI systems.

Shapley values can reveal which quantum gates matter most: The paper applies Shapley values (a well-established fairness concept from game theory) to quantum circuits, allowing researchers to identify which individual gates or groups of gates are most important for a circuit's performance on a given task.

Better circuit design through interpretability: By understanding which gates contribute most to a quantum circuit's success, researchers can design more efficient quantum machine learning systems and avoid including unnecessary components—potentially reducing computational costs and improving performance.

Bridging quantum and classical AI transparency: This work extends explainable AI principles from classical machine learning into the quantum domain, helping ensure that as quantum computing becomes more practical, it maintains the same transparency and interpretability standards expected of modern AI systems.

Human understanding of quantum systems remains a bottleneck: The paper highlights that even as quantum hardware improves, our ability to comprehend how these systems work lags behind—making explainability research essential for building trust and enabling effective use of quantum machine learning in real-world applications.