On the Interpretability of Quantum Neural Networks

TL;DR

This paper investigates the interpretability of quantum neural networks by adapting classical explanation methods, notably introducing Q-LIME, to enhance understanding of quantum AI models.

Contribution

It generalizes the classical LIME technique to quantum neural networks, providing a new method for explaining quantum AI models.

Findings

Q-LIME offers explanations for quantum neural networks.

The method delineates regions with random labels influenced by quantum measurements.

This work advances responsible and accountable quantum AI development.

Abstract

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.