Classification method based on machine learning framework and related device

Abstract

The invention discloses a classification method based on a machine learning framework and a related device, a quantum machine learning classification model is constructed by calling a quantum module included in the machine learning framework, and the quantum machine learning classification model comprises a ground state data coding quantum circuit, a QVC and a measurement quantum circuit which are cascaded. The ground state data coding quantum circuit is used for coding input data to a ground state of a quantum bit, and the number of logic gates included in the ground state data coding quantum circuit is smaller than that of other data coding quantum circuits such as an amplitude data coding quantum circuit, an angle data coding quantum circuit and an IQP data coding circuit. Therefore, the line depth of the data coding quantum line is reduced, the line depth of the quantum line in the quantum machine learning classification model is further reduced, the error probability of quantum bits is reduced, and the classification accuracy of the quantum machine learning classification model is improved.

Description

technical field [0001] The invention belongs to the technical field of quantum computing, and in particular relates to a classification method and a related device based on a machine learning framework. Background technique [0002] Classical machine learning has revolutionized many subfields of artificial intelligence and achieved significant success. In recent years, with the advent of the information age, machine learning has developed rapidly. The rapid growth of electronic data volumes has led to a massive increase in training data for machine learning models. At the same time, the rapid development of computer computing power, especially the emergence of a series of new electronic computing devices represented by Graphics Processing Unit (GPU), has made large-scale training of machine learning models a reality. Therefore, machine learning has greatly surpassed the previous traditional algorithms and has been widely used in many fields. Machine learning has achieved ...

AI Technical Summary

Problems solved by technology

However, the qubits in quantum computing are extremely fragile and are susceptible to errors caused by noise. The probability of qubit errors is proportional to the depth of the quantum circuit. The deeper the circuit, the greater the probability of qubit errors

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