The clever hybridization of quantum computing concepts and evolutionary algorithms (EAs) resulted in a new field called quantum-inspired evolutionary algorithms (QIEAs). Unlike traditional EAs, QIEAs employ quantum bits to adopt a probabilistic representation of the state of a feature in a given solution. This unprecedented feature enables them to achieve better diversity and perform global search, effectively yielding a tradeoff between exploration and exploitation. We conducted a comprehensive survey across various publishers and gathered 56 papers. We thoroughly analyzed these publications, focusing on the novelty elements and types of heuristics employed by the extant quantum-inspired evolutionary algorithms (QIEAs) proposed to solve the feature subset selection (FSS) problem. Importantly, we provided a detailed analysis of the different types of objective functions and popular quantum gates, i.e., rotation gates, employed throughout the literature. Additionally, we suggested several open research problems to attract the attention of the researchers.

翻译：量子计算概念与进化算法的巧妙融合催生了一个新兴领域——量子启发生成算法。与传统进化算法不同，量子启发生成算法采用量子比特对特征状态进行概率化表征。这一创新特性使其能够实现更优的种群多样性并执行全局搜索，从而有效平衡探索与利用的矛盾关系。本研究系统检索了多家出版机构的文献，共收集56篇相关论文。通过对这些文献的深入分析，我们聚焦于现有量子启发生成算法在解决特征子集选择问题时所采用的新颖要素与启发式策略类型。特别地，我们对文献中广泛使用的各类目标函数及主流量子门（如旋转门）进行了细致剖析。此外，本文提出了若干待解决的研究课题，以期吸引学界关注。