Quantum cluster algorithm for data classification

Abstract We present a quantum algorithm for data classification based on the nearest-neighbor learning algorithm. The is divided into two steps: Firstly, in same class smaller groups with sublabels assisting building boundaries between different labels. Secondly we construct circuit that contains multi control gates. easy to implement and efficient predicting labels of test data. To illustrate power efficiency this approach, phase transition diagram metal-insulator VO 2 , using limited trained experimental data, where typical strongly correlated electron materials, metallic-insulating has drawn much attention condensed matter physics. Moreover, demonstrate our randomly generated entanglement various Werner states, training sets can not be by single curve, instead, more than one curves are required separate them apart perfectly. Our preliminary result shows considerable potential problems, particularly constructing phases materials.