This work focuses on developing efficient post-hoc explanations for quantum AI algorithms. In classical contexts, the cooperative game theory concept of the Shapley value adapts naturally to post-hoc explanations, where it can be used to identify which factors are important in an AI's decision-making process. An interesting question is how to translate Shapley values to the quantum setting and whether quantum effects could be used to accelerate their calculation. We propose quantum algorithms that can extract Shapley values within some confidence interval. Our method is capable of quadratically outperforming classical Monte Carlo approaches to approximating Shapley values up to polylogarithmic factors in various circumstances. We demonstrate the validity of our approach empirically with specific voting games and provide rigorous proofs of performance for general cooperative games.

翻译：本研究致力于为量子人工智能算法开发高效的事后解释方法。在经典情境中，合作博弈论的Shapley值概念能自然地适用于事后解释，可用于识别在人工智能决策过程中哪些因素具有重要性。一个值得探讨的问题是如何将Shapley值移植到量子场景中，以及量子效应能否用于加速其计算过程。我们提出了能在特定置信区间内提取Shapley值的量子算法。在各种情况下，我们的方法在近似计算Shapley值时能以多项式对数因子的优势实现相对于经典蒙特卡洛方法的二次加速。我们通过具体投票博弈实验验证了方法的有效性，并为一般合作博弈提供了严格的性能证明。