Classification method and related device based on machine learning framework

Abstract

The invention discloses a classification method based on a machine learning framework and a related device. The invention constructs a quantum machine learning classification model by calling the quantum modules included in the machine learning framework. The quantum machine learning classification model includes cascaded data encoding quantum circuits, Ansatz Quantum circuits and measurement quantum circuits, data encoding quantum circuits for encoding input data into quantum states of qubits, Ansatz quantum circuits for evolving quantum states of qubits to target states, and measurement quantum circuits for measuring said qubits Obtain the measurement data; then input the data to be classified as input data into the machine learning classification model to obtain the measurement data, and classify the data to be classified based on the measurement data; the entire classification is completely realized by quantum circuits, and by using the nature of quantum superposition in quantum computing, it can Reduce the occupation of resources such as bits and improve computing efficiency.

Description

technical field [0001] The invention belongs to the technical field of quantum computing, and in particular relates to a classification method and related device based on a machine learning framework. Background technique [0002] Classical machine learning has revolutionized many subfields of artificial intelligence with significant success. In recent years, with the advent of the information age, machine learning has developed rapidly. The rapid growth in the amount of electronic data has led to a massive increase in training data for machine learning models. At the same time, the rapid development of computer computing capabilities, especially the emergence of a series of new electronic computing devices represented by Graphics Processing Units (GPUs), 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 reac...

AI Technical Summary

Problems solved by technology

[0003] However, as the number of samples and the number of features increase, the classic machine learning classification model will consume a lot of computing resources, which limits its application to a certain extent.

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