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.

翻译：在经典语境中，合作博弈论中的夏普利值概念已被用于机器学习模型的事后解释。然而，这种方法难以直接应用于可解释量子机器学习（XQML）。求解夏普利值可能具有极高的计算复杂度。我们提出了能够在特定置信区间内提取夏普利值的量子算法。我们的算法在多项式时间内运行。我们通过在合作投票博弈的具体实例中验证了每种方法的有效性。