Identification method, system and device based on quantum network and storage medium

Abstract

The invention discloses an identification method based on a quantum network. The method comprises the following steps: constructing a group of Hamiltonian quantities with inappropriate operators and generating a quantum network to be optimized; inputting training data to the quantum network and performing quantum annealing to obtain a measurement result for the training data; comparing the obtained measurement result with a theoretical result to judge whether the measurement result meets a preset precision requirement or not; if not, adjusting the quantum network, and returning to execute the operation of inputting the training data to the quantum network and performing quantum annealing; if yes, taking the current quantum network as a trained quantum network; and inputting to-be-identified data into the trained quantum network, and obtaining an identification result of the to-be-identified data. By applying the scheme of the invention, global optimization can be realized, and the performance of the obtained quantum network can be improved. The invention also discloses an identification system and equipment based on the quantum network, and a storage medium, which have corresponding technical effects.

Description

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AI Technical Summary

Benefits of technology

This technology allows users to creatively optimize their own quantum systems by optimizing certain parameters like time or space usage based on specific goals they want them working with (such as improving efficiency). These modifications help improve the quality of these systems' output signals compared to existing ones without any changes. Overall, it provides better ways to train more powerful quantum devices while maintaining good overall performance over longer periods of time.

Problems solved by technology

This patents discuss various techniques used to enhance the efficiency of training quantum computer models like qnetic nets. These techniques aim at improving their accuracy over traditional ways but they may lead to better results when compared against other approaches due to factors including complexity involved in implementing them.

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