Quantum computing is a promising paradigm based on quantum theory for performing fast computations. Quantum algorithms are expected to surpass their classical counterparts in terms of computational complexity for certain tasks, including machine learning. In this paper, we design, implement, and evaluate three hybrid quantum k-Means algorithms, exploiting different degree of parallelism. Indeed, each algorithm incrementally leverages quantum parallelism to reduce the complexity of the cluster assignment step up to a constant cost. In particular, we exploit quantum phenomena to speed up the computation of distances. The core idea is that the computation of distances between records and centroids can be executed simultaneously, thus saving time, especially for big datasets. We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version, still obtaining comparable clustering results.

翻译：量子计算是基于量子理论进行快速计算的一个很有希望的范例。 量子算法在计算复杂性方面预计将超过其传统的对应方, 包括机器学习。 在本文中, 我们设计、 实施和评估三种混合量子 k- 计量算法, 利用不同程度的平行论。 事实上, 每种算法都逐渐利用量子平行论来降低集群任务的复杂性, 逐渐提升到一个不变的成本。 特别是, 我们利用量子现象来加快距离计算。 核心理念是记录和核固醇之间的距离计算可以同时进行, 从而节省时间, 特别是大数据集的时间。 我们显示, 我们的混合量子 k- 计量算法可以比古典版本更有效, 仍然获得可比的组合结果 。