A text classification method and an image classification method

By using a quantum classifier based on support vector machines and utilizing quantum matrix inversion algorithms and unitary matrix operations, the problems of slow computing speed and unstable accuracy of traditional machine learning algorithms in high-dimensional data processing are solved, and the efficient application of quantum computing in data classification is realized.

CN119272889BActive Publication Date: 2025-09-09NANJING UNIV OF INFORMATION SCI & TECH
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Patent Information

Application Number
CN202411765271.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-12-04
Publication Date
2025-09-09
Estimated Expiration
2044-12-04

AI Technical Summary

Technical Problem

Traditional machine learning algorithms have slow computing speeds and unstable accuracy when processing high-dimensional data. Quantum support vector machines increase the size of quantum circuits under high-order kernel function schemes, have high computing equipment requirements, are difficult to optimize parameters, and have poor flexibility.

Method used

A quantum classifier based on support vector machine is used. Through quantum matrix inversion algorithm and quantum feature mapping, the SVM optimization problem is transformed into an equivalent linear algebra problem. Quantum circuits are used to realize data classification, and quantum matrix inversion algorithm and unitary matrix operations are used to construct a quantum classifier.

Benefits of technology

It improves the training speed and accuracy of high-dimensional data processing, reduces the complexity of quantum circuits, realizes the efficient application of quantum computing in the field of data classification, and broadens the scope of application.

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Abstract

The present invention discloses a quantum classifier based on a support vector machine, comprising: converting the optimization problem of an SVM into an equivalent linear algebra problem; utilizing quantum feature mapping to store data in a quantum state, thereby allowing a quantum computer to process data analysis; proposing a quantum matrix inversion algorithm to solve the linear algebra problem, constructing a quantum classifier, and realizing data classification; the present invention combines traditional solutions with quantum algorithms, leveraging the advantages of quantum computing, improving the training speed of the support vector machine in processing complex problems and ensuring computational accuracy, thereby breaking through the bottleneck problem of traditional solutions.
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