量子机器学习实现方法的研究及其在QPU上的发展

V. Potapov, S. Gushanskiy
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摘要

在过去的几十年里,量子计算领域取得了重大突破。人们对量子信息系统的研究越来越感兴趣,最近导致了量子信息系统原型和开发方法的发展。本文描述了量子神经网络的可能性、优缺点。对具有两种不同特征映射的经典前馈神经网络和量子神经网络的吞吐量、学习复杂度进行了分析和比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research of Implementation Methods for Quantum Machine Learning and Their Development on QPU
Over the past few decades, there has been a significant breakthrough in the field of quantum computing. Research is of growing interest, which has recently led to the development of quantum information systems prototypes and methods for their development. The paper describes the possibilities, advantages and disadvantages of quantum neural networks. The analysis of throughput, learning complexity and comparison of a classical feed-forward neural network and a quantum neural network with two different feature maps is carried out.
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