In this paper, we inaugurate the field of quantum fair machine learning. We undertake a comparative analysis of differences and similarities between classical and quantum fair machine learning algorithms, specifying how the unique features of quantum computation alter measures, metrics and remediation strategies when quantum algorithms are subject to fairness constraints. We present the first results in quantum fair machine learning by demonstrating the use of Grover's search algorithm to satisfy statistical parity constraints imposed on quantum algorithms. We provide lower-bounds on iterations needed to achieve such statistical parity within $\epsilon$-tolerance. We extend canonical Lipschitz-conditioned individual fairness criteria to the quantum setting using quantum metrics. We examine the consequences for typical measures of fairness in machine learning context when quantum information processing and quantum data are involved. Finally, we propose open questions and research programmes for this new field of interest to researchers in computer science, ethics and quantum computation.

翻译：在本文中,我们开创了量子公平机器学习领域。我们对古典和量子公平机器学习算法之间的差异和相似之处进行了比较分析,具体说明量子计算的独特特点如何在量子算法受到公平限制的情况下改变措施、计量和补救战略。我们展示了量子公平机器学习的第一批成果,展示了格罗弗搜索算法的使用,以满足量子算法在统计上对等性的限制。我们提供了在美元容忍范围内实现这种统计等同所需迭代数的下限。我们将卡通利普施茨规定的个人公平标准扩大到使用量子计量法的量子设置。我们研究了在涉及量子信息处理和量子数据时,在机器学习背景方面典型的公平衡量结果。最后,我们为计算机科学、伦理和量子计算方面的研究人员提出了这个新的关注领域公开的问题和研究方案。