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Journal of Military Science and Technology

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

Bach Nhat Hoang, Dang Tien Sy, Doan Trung Thanh, Phan Huy Anh

Published Oct 30, 2025• 0 citations
Publisher

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.

DOI
10.54939/1859-1043.j.mst.iite.2025.19-26

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We present the application of KeplerQ-Max, a hybrid quantum–classical machine learning platform built on the Quantum Approximate Optimization Algorithm (QAOA), to the problem of underwater acoustic target recognition using passive sonar data. The system employs a 25-qubit QAOA ansatz executed on IBM Heron R2 real quantum hardware (ibm_fez) to learn discriminative feature representations across three threat classes: Hostile, Unknown, and Friendly. Applied to a dataset of 5,000 sonar records, KeplerQ-Max completed full inference in 64 seconds (12.8 ms per record), classifying 73.58% of targets as Friendly, 6.14% as Hostile, and 20.28% as Unknown, with the Unknown class achieving a top posterior confidence of 98.9%. A total of 1,321 records (26.42%) were flagged as requiring operational action. The Hostile class achieved a mean quantum circuit score of 0.684 and a peak score of 0.806, while 101 high-confidence Unknown records (confidence ≥ 0.90) were identified as priority candidates for secondary sensor fusion. The model (kepler-qmax/qaoa-pytorch-25q9l) integrates a quantum-parameterized variational circuit with a PyTorch post-processing layer, enabling noise-resilient inference on near-term quantum devices at approximately 78 records per second. These results demonstrate that QAOA-based quantum machine learning achieves practical classification throughput on real sonar data without classical simulation, opening pathways for quantum-enhanced naval threat assessment and maritime security applications. Email: research@sonicium.ltd
Quantum computing, based on quantum theory, holds great promise as an advanced computational paradigm for achieving 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 degrees 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 are theoretically faster than the classical algorithm, while experiments suggest that it is possible to obtain comparable clustering results.
This paper investigates the application of quantum algorithms to enhance clustering performance on big data. Traditional clustering algorithms often struggle with the computational complexity associated with large datasets and intricate data distributions. We propose and analyze the implementation of quantum k-means and quantum hierarchical clustering algorithms, leveraging quantum superposition and entanglement to potentially achieve significant speedups. The theoretical framework outlines the core concepts and mathematical formulations of these algorithms. We explore the potential for quantum algorithms to address the limitations of classical approaches, particularly in scenarios involving high-dimensional data and complex clusters. The research focuses on the algorithmic design, examining the key steps involved in transforming classical clustering problems into quantum computations. This work contributes to the emerging field of quantum data mining and provides a foundational understanding of how quantum computing can revolutionize data analysis tasks. The primary focus is on the theoretical aspects of quantum clustering and does not include experimental validation.
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