Explicability and Inexplicability in the Interpretation of Quantum
  Neural Networks
Lirandë Pira Chris Ferrie
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.