On the Interpretability of Quantum Neural Networks
This work addresses interpretability for quantum AI models, which is crucial for building trusted systems, but it is incremental as it adapts existing classical methods to the quantum setting.
The paper tackles the interpretability problem in quantum neural networks by generalizing the classical LIME technique to create Q-LIME, which provides explanations and identifies regions where data samples receive random labels due to quantum measurements.
Interpretability of artificial intelligence (AI) methods, particularly deep neural networks, is of great interest. This heightened focus stems from the widespread use of AI-backed systems. These systems, often relying on intricate neural architectures, can exhibit behavior that is challenging to explain and comprehend. The interpretability of such models is a crucial component of building trusted systems. Many methods exist to approach this problem, but they do not apply straightforwardly to the quantum setting. Here, we explore the interpretability of quantum neural networks using local model-agnostic interpretability measures commonly utilized for classical neural networks. Following this analysis, we generalize a classical technique called LIME, introducing Q-LIME, which produces explanations of quantum neural networks. A feature of our explanations is the delineation of the region in which data samples have been given a random label, likely subjects of inherently random quantum measurements. We view this as a step toward understanding how to build responsible and accountable quantum AI models.