Research and evaluation of underwater acoustic signal classification capability using quantum K-mean algorithm on real dataset

Abstract

In the big data era, traditional machine learning algorithms like K-means face computational challenges in processing large, high-dimensional datasets, particularly for underwater acoustic signal classification. This study investigates the application of the quantum clustering algorithm Q-means, a quantum-enhanced variant of K-means, to real passive sonar datasets. The purpose is to evaluate Q-means' effectiveness in classifying propeller-driven ship signals under noisy conditions, leveraging quantum principles such as superposition and entanglement for exponential speedup. The research extends classical K-means to δ-k-means for robustness, implementing quantum subroutines including distance estimation, cluster assignment, and state tomography. Preprocessed features from power spectral density analysis of real datasets (from prior studies on varying ship speeds) are used as input to avoid issues with raw time-series data, such as high dimensionality and sensitivity to shifts. Simulations on 15,000 passive sonar samples demonstrate that Q-means achieves clustering quality comparable to K-means, with clear separation of three clusters and accurate centroids, while reducing complexity from O(n) to O(polylog(n)). This validates Q-means as a promising tool for large-scale, noisy acoustic data in national security applications, bridging quantum theory with practical machine learning.