Quantum clustering method and related devices based on machine learning framework

Abstract

The invention discloses a quantum clustering method and a related device based on a machine learning framework. The invention creates a quantum machine learning clustering model based on acquired sample data sets and k initial clustering centers by calling a quantum module, and runs the quantum Machine learning clustering model to obtain output data; then call the data structure module to determine the similarity between each sample data in the sample data set and the k initial cluster centers based on the output data, and then realize the data in the classic clustering algorithm The comparison of the distance between the qubits is converted into the comparison of the similarity between the quantum states of the qubits, and the use of the nature of the quantum superposition reduces the occupancy rate of the computing resources of the classical k-means algorithm; finally, based on the similarity, each of the sample data is determined The target cluster to which it belongs realizes the improvement of the speed of the clustering algorithm.

Description

technical field [0001] The invention belongs to the technical field of quantum computing, and in particular relates to a quantum clustering method and 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, and the sample data in the cluster is divided into k clusters. Cluster as close as possible, and make the distance between clusters as large as possible. The smaller the distance between the data, the greater the similarity; the greater the distance between the data, the smaller the similarity, and its function is to cluster samples with similar characteristics into one class. [0003] However, as the number of sample data sets and cluster centers increases, the classical k-means algorithm will consume a lot of compu...

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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