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arXiv · 2607.21919

Quantum multi-label k-nearest neighbor

Yilin Shen·Qin Liao
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Abstract

Although multi-label k-nearest neighbor (ML-kNN) is able to effectively solve multi-label learning (MLL) problem with local neighborhood similarity, its time complexity is nearly unacceptable with large-scale datasets. To solve this issue, we propose a novel ML-kNN algorithm with quantum computing techniques, which called quantum multi-label k-nearest neighbor (QML-kNN). In particular, we first accelerate the calculation of the prior probability by taking advantage of quantum phase estimation and Grover's amplitude amplification. Then, a controlled-SWAP test and a quantum k-maximal similarity search are used for efficiently identifying the neighbors. Subsequently, a quantum parallel counting circuit (QPCC) is designed to rapidly calculate the posterior probabilities. Experimental results demonstrate that QML-kNN is able to significantly reduce the time complexity of solving multi-label problems with performance improvement, achieving a substantial speedup over the classical MLL algorithm.

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Yilin Shen, Qin Liao. 2026-07-24. Quantum multi-label k-nearest neighbor. https://arxiv.org/abs/2607.21919

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