Quantum clustering method based on machine learning framework and related device

Abstract

The invention discloses a quantum clustering method based on a machine learning framework and a related device, and the method comprises the steps: building a quantum machine learning clustering model based on an obtained sample data set and k initial clustering centers through calling a quantum module, and operating the quantum machine learning clustering model to obtain output data; and then, a data structure module is called to determine the similarity between each piece of sample data in a sample data set and the k initial clustering centers based on the output data, so that comparison of distances between data in a classical clustering algorithm is converted into comparison of similarity between quantum states of quantum bits, and the similarity between the quantum states of the quantum bits is obtained by utilizing the property of quantum superposition. The occupancy rate of a classical k-means algorithm on computing resources is reduced; and finally, determining a target cluster to which each piece of sample data belongs based on the similarity, so that the speed of a clustering algorithm is improved.

Description

technical field [0001] The invention belongs to the technical field of quantum computing, and in particular relates to a quantum clustering method and a related device based on a machine learning framework. Background technique [0002] The k-means algorithm is the most commonly used clustering algorithm in unsupervised machine learning. For a given sample data set, according to the distance between the sample data, the sample data set is divided into k clusters, so that the sample data in the cluster Gather together as closely as possible, and let the distance between clusters be as large as possible. The smaller the distance between the data, the greater the similarity; the larger the distance between the data, the smaller the similarity, and its role is to cluster samples with similar characteristics into one class. [0003] However, as the number of sample data sets and cluster centers increases, the classic k-means algorithm will consume a lot of computing resources, a...

AI Technical Summary

Problems solved by technology

[0003] However, as the number of sample data sets and cluster centers increases, the classic k-means algorithm will consume a lot of computing resources, and the calculation speed will become slower and slower.

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