Explicability and Inexplicability in the Interpretation of Quantum Neural Networks
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Interpretability of artificial intelligence (AI) methods, particularly deep neural networks, is of great interest due to the widespread use of AI-backed systems, which often have unexplainable behavior. The interpretability of such models is a crucial component of building trusted systems. Many methods exist to approach this problem, but they do not obviously generalize to the quantum setting. Here we explore the interpretability of quantum neural networks using local model-agnostic interpretability measures of quantum and classical neural networks. We introduce the concept of the band of inexplicability, representing the interpretable region in which data samples have no explanation, likely victims of inherently random quantum measurements. We see this as a step toward understanding how to build responsible and accountable quantum AI models.
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