We introduce a novel hypothesis in the field of outlier detection, suggesting that normal data tend to be distributed in regions where the density changes smoothly or is less pronounced, whereas abnormal data often exhibit distribution in areas characterized by abrupt changes in data density. Relying on this hypothesis, we develop a novel density-based unsupervised outlier detection method, referred to as Quantum Clustering (QC). This approach addresses the processing of unlabeled data and employs a potential function to identify the centroids of clusters and outliers effectively. Experimental results demonstrate that the potential function can accurately detect hidden outliers within data points. Furthermore, by adjusting the parameter [Formula: see text], QC enables the identification of more subtle outliers. Additionally, our method is evaluated on several benchmarks from diverse research areas, affirming its broad applicability.