On the Interpretability of Quantum Neural Networks

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.

References in corpus (6)

• Variational Quantum Algorithms
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• A Quantum Algorithm for Shapley Value Estimation
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• IQNN-CS: Interpretable Quantum Neural Network for Credit Scoring
• Spectral analysis of the Koopman operator as a framework for recovering Hamiltonian parameters in open quantum systems