Binary classification method based on quantum twin neural network and face recognition method thereof

A neural network and quantum neural technology, applied in the quantum field, can solve the problems of low reliability and precision, restricting classification accuracy and application, etc.

Pending Publication Date: 2020-07-03
CENT SOUTH UNIV
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  • Abstract
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AI Technical Summary

Problems solved by technology

[0004] However, the reliability and accuracy of the current Siamese network classification methods are not high, which seriously restricts the classification accuracy and application

Method used

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  • Binary classification method based on quantum twin neural network and face recognition method thereof
  • Binary classification method based on quantum twin neural network and face recognition method thereof
  • Binary classification method based on quantum twin neural network and face recognition method thereof

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Embodiment Construction

[0090] Such as figure 1 Shown is the method flow diagram of the present invention:

[0091] figure 1 is the structure diagram of the classic twin neural network, image 3 The quantum twin neural network structure diagram designed for the present invention highlights the structural similarity of classical and quantum twin neural networks, and according to image 3 Constructing a general model of quantum twin networks in the case of an auxiliary qubit. In particular, in order to clearly show the structure of the quantum twin network and its classification ideas, the image 3 Chinese network Defined as a quantum neural network, such as Figure 4 As shown, the simple string binary classification problem is realized by constructing a quantum twin neural network model.

[0092] A typical application scenario of the present invention is: for a classic data set composed of binary strings of equal length Classification, where any string z i Any character z in ij Expressed as...

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Abstract

The invention discloses a binary classification method based on a quantum twin neural network. The method comprises the following steps: setting input data of a classification model; constructing a quantum neural network model, a quantum twin neural network model and a loss function model for training; learning and training the quantum twin neural network model according to the loss function modelto obtain a final quantum twin neural network classification model; and carrying out dichotomy on the to-be-classified data by adopting the quantum twin neural network classification model. The invention further discloses a face recognition method comprising the binary classification method based on the quantum twin neural network. According to the binary classification method based on the quantum twin neural network and the face recognition method of the binary classification method, the quantum twin neural network is adopted to carry out binary classification on the data, rapid binary classification of the data is achieved, and the method is simple, rapid, high in reliability and good in accuracy.

Description

technical field [0001] The invention belongs to the quantum field, and in particular relates to a binary classification method based on a quantum twin neural network and a face recognition method thereof. Background technique [0002] In recent years, the application of machine learning in classification and other fields has gradually attracted people's attention. Recent attention-grabbing classification methods, such as deep learning, usually require a known and limited training set of categories, and require large-scale data for training. In order to meet such application scenarios: the number of categories is very large, only part of the categories are known, and the number of samples in each category is small, Chopra et al. proposed to learn similarity measures through Siamese networks for tasks such as recognition or verification. The Siamese network is a network structure used for similarity measurement. It has shown good results for classification problems such as re...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N10/00G06N3/04G06N3/08G06K9/00
CPCG06N10/00G06N3/08G06V40/172G06N3/045G06F18/2414
Inventor 石金晶陆玉虎陈鹭冯艳艳陈淑慧施荣华
Owner CENT SOUTH UNIV
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