Quantum multi-label k-nearest neighbor

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.

arXiv:2607.21919 [quant-ph]

(or arXiv:2607.21919v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2607.21919 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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