The introduction of the European Union's (EU) set of comprehensive regulations relating to technology, the General Data Protection Regulation, grants EU citizens the right to explanations for automated decisions that have significant effects on their life. This poses a substantial challenge, as many of today's state-of-the-art algorithms are generally unexplainable black boxes. Simultaneously, we have seen an emergence of the fields of quantum computation and quantum AI. Due to the fickle nature of quantum information, the problem of explainability is amplified, as measuring a quantum system destroys the information. As a result, there is a need for post-hoc explanations for quantum AI algorithms. In the classical context, the cooperative game theory concept of the Shapley value has been adapted for post-hoc explanations. However, this approach does not translate to use in quantum computing trivially and can be exponentially difficult to implement if not handled with care. We propose a novel algorithm which reduces the problem of accurately estimating the Shapley values of a quantum algorithm into a far simpler problem of estimating the true average of a binomial distribution in polynomial time.

翻译：欧盟颁布的综合性技术法规《通用数据保护条例》赋予欧盟公民对对其生活产生重大影响的自动化决策要求解释的权利。这构成了重大挑战，因为当今许多最先进的算法通常是无法解释的黑箱。与此同时，量子计算和量子AI领域也崭露头角。由于量子信息的易变性，测量量子系统会破坏信息，可解释性问题因此被放大。这促使我们需要对量子AI算法进行事后解释。在经典语境中，合作博弈论中的沙普利值概念已被应用于事后解释。然而，这种方法无法直接应用于量子计算，且若处理不当，其实现难度可能呈指数级增长。我们提出了一种新型算法，该算法可将准确估计量子算法沙普利值的问题，简化为在多项式时间内估计二项分布真实均值这一远为简单的问题。