Quantum machine learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. It generally exposes the synthesis of important machine learning algorithms in a quantum framework. Dimensionality reduction of a dataset with a suitable feature selection strategy is one of the most important tasks in knowledge discovery and data mining. The efficient feature selection strategy helps to improve the overall accuracy of a large dataset in terms of machine learning operations. In this paper, a quantum feature selection algorithm using a graph-theoretic approach has been proposed. The proposed algorithm has used the concept of correlation coefficient based graph-theoretic classical approach initially and then applied the quantum Oracle with CNOT operation to verify whether the dataset is suitable for dimensionality reduction or not. If it is suitable, then our algorithm can efficiently estimate their high correlation values by using quantum parallel amplitude estimation and amplitude amplification techniques. This paper also shows that our proposed algorithm substantially outperforms than some popular classical feature selection algorithms for supervised classification in terms of query complexity of \(O(\frac {k\sqrt {N_{c}^{(k)}N_{f}^{(k)}}}{\epsilon })\), where N is the size of the feature vectors whose values are ⩾ TH_min(minimum threshold), k is the number of iterations and where 𝜖 is the error for estimating those feature vectors. Compared with the classical counterpart, i.e. the performance of our quantum algorithm quadratically improves than others.

量子机器学习弥合了量子计算的抽象发展与机器学习应用研究之间的鸿沟。它通常在量子框架中公开重要机器学习算法的综合。使用合适的特征选择策略降低数据集的维数是知识发现和数据挖掘中最重要的任务之一。有效的特征选择策略有助于在机器学习操作方面提高大型数据集的整体准确性。本文提出了一种基于图论的量子特征选择算法。该算法首先使用了基于相关系数的图论经典方法的概念，然后将其与CNOT运算结合应用了量子Oracle来验证数据集是否适合降维。如果合适的话，我们的算法可以通过使用量子并行幅度估计和幅度放大技术有效地估计它们的高相关值。本文还表明，就监督分类的查询复杂度而言，我们提出的算法在监督分类方面明显优于某些流行的经典特征选择算法。\（O（\ frac {k \ sqrt {N_ {c} ^ {（k）} N_ {f} ^ {（k）}}} {\ epsilon}）\），其中N是特征向量的大小其值⩾ Ť ħ_中号我ñ（最小阈值），k是迭代次数，并且其中ε是用于估计那些特征向量的误差。与经典方法相比，我们的量子算法的性能比其他方法提高了两倍。