Quantum neural network method and system for image recognition and medium

Abstract

The invention provides a quantum neural network method and system for image recognition, and a medium, and relates to the technical field of quantum neural network methods, and the method comprises the steps: quantum state coding: preprocessing MNIST data set data, and converting the MNIST data set data into angle information corresponding to the operation of a rotating door according to a processing result; constructing a quantum neural network: optimizing the learning process of the quantum neural network through decomposition calculation; and characterizing a measurement result: finding out the quantum state with the maximum probability through the measurement result to realize image recognition. According to the invention, the structure logic of the quantum neural network is clearer, the realization is easy, and the learning efficiency is higher.

Description

technical field [0001] The present invention relates to the technical field of quantum neural network methods, in particular to a quantum neural network method, system and medium for image recognition. Background technique [0002] Quantum neural network method is the combination of quantum method and artificial neural network. Quantum computing uses the quantum effects of superposition and entanglement in the microscopic world to solve computational problems in mathematics. Compared with the classical method, the quantum method can provide exponential speed-up ability on the problem of finding hidden subgroups, and can provide geometric speed-up ability on the search problem. Therefore, the focus of quantum method research has always been on how to better expand this exponential and geometric acceleration capabilities. However, on other issues, such as machine learning, preliminary studies have shown that quantum methods have other advantages, such as Better classificatio...

AI Technical Summary

Problems solved by technology

[0004] In view of the above existing technologies, there are also many researches on quantum neural networks in the world, such as figure 1 As shown, the quantum neural network structure in the prior art is shown. It can be seen from the figure that the quantum gate G used in the network is an arbitrary unitary transformation, and the double-bit quantum gate is a controlled G gate. It can be seen that the existing quantum The logic of the neural network structure is not clear enough, it is not easy to implement, and the learning efficiency is low

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