A Quantum Algorithm for Shapley Value Estimation

Abstract

In the classical context, the cooperative game theory concept of the Shapley value has been adapted for post hoc explanations of machine learning models. However, this approach does not easily translate to eXplainable Quantum ML (XQML). Finding Shapley values can be highly computationally complex. We propose quantum algorithms which can extract Shapley values within some confidence interval. Our results perform in polynomial time. We demonstrate the validity of each approach under specific examples of cooperative voting games.

9 pages, 4 figures, 24 references, preprint of QCE 2023 (IEEE International Conference on Quantum Computing and Engineering) Technical Paper (Quantum Algorithms for Shapley Value Calculation), available at https://doi.org/10.1109/QCE57702.2023.00024