@inproceedings{68c60262448b4788b9d9fe642cd48d2a,
title = "Quantum Algorithms for Shapley Value Calculation",
abstract = "In the classical context, the cooperative game theory concept of the Shapley value has been adapted for post hoc explanations of Machine Learning (ML) 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.",
keywords = "Artificial Intelligence, Cooperative Game Theory, Explainable Quantum Machine Learning, Machine Learning, Quantum Computing, Quantum Machine Learning, Shapley Value",
author = "Iain Burge and Michel Barbeau and Joaquin Garcia-Alfaro",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 4th IEEE International Conference on Quantum Computing and Engineering, QCE 2023 ; Conference date: 17-09-2023 Through 22-09-2023",
year = "2023",
month = jan,
day = "1",
doi = "10.1109/QCE57702.2023.00024",
language = "English",
series = "Proceedings - 2023 IEEE International Conference on Quantum Computing and Engineering, QCE 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "142--150",
editor = "Hausi Muller and Yuri Alexev and Andrea Delgado and Greg Byrd",
booktitle = "Proceedings - 2023 IEEE International Conference on Quantum Computing and Engineering, QCE 2023",
}