Entanglement Partitioning of Quantum Particles for Data Clustering
Dianxun Shuai, Cunpai Lu, Bin Zhang · 2006
This paper presents a generalized quantum particle model to greatly quicken and improve data clustering. The proposed model uses the random dynamics and quantum entanglement of quantum particles on a particle array. In comparison with classical nonquantum methods, the quantum particle model not only clusters much faster, but also has better clustering quality for multi-shape multi-distribution high-dimensional large-scale data sets with noise. The simulations and comparisons show the effectiveness of the quantum particle model