Publications
Talk · IEEE International Conference on Smart Computing Workshops and Other Affiliated Events · June 1, 2026
Quantum k-means based on Quantum Local Sensitive Hashing with Application to Irrigation Machine Sensors Data Clustering
Kamil Ravilevich Khadiev, Aliya Khadieva
Abstract
A k-means algorithm is one of the popular algorithms of Machine learning for clustering problem. In this paper, we present a quantum version of modification of the algorithm that has O(N$\sqrt {dk} $+Nm) query complexity for each iteration and a small size of quantum memory, which is O(logd+logk+logm). Here N is the number of objects, d is the number of attributes (or features), k is the number of clusters, and m is the number of bits in the binary representation of one attribute. We apply the algorithm for clustering data that coming from sensors of an irrigation machine.
Quantum Computing Algorithms and ArchitectureAdvanced Clustering Algorithms ResearchQuantum Information and Cryptographyquantum clusteringquery complexityquantum memory
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