Progress in Cloud-based Quantum Machine Learning

Yang Junhong; Wang Banghai; Zhong Zhi

doi:10.12052/gdutxb.240118

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Progress in Cloud-based Quantum Machine Learning

更新时间：2026-05-27

- Progress in Cloud-based Quantum Machine Learning
- Vol. 42, Issue 3, Pages: 1-11(2025)
- 作者机构：
  
  1. 广东工业大学计算机学院,广东,广州,510006
  2. 广东工业大学继续教育学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.240118
  CLC： TP305;TP399
- Published：2025
- 稿件说明：
移动端阅览
杨俊鸿, 王帮海, 钟志. 云量子机器学习研究进展[J]. 广东工业大学学报, 2025, 42(3): 1–11.

Yang Junhong, Wang Banghai, Zhong Zhi. Progress in Cloud-based Quantum Machine Learning[J]. 2025, 42(3): 1-11.
杨俊鸿, 王帮海, 钟志. 云量子机器学习研究进展[J]. 广东工业大学学报, 2025, 42(3): 1–11. DOI： 10.12052/gdutxb.240118

Yang Junhong, Wang Banghai, Zhong Zhi. Progress in Cloud-based Quantum Machine Learning[J]. 2025, 42(3): 1-11. DOI： 10.12052/gdutxb.240118.

摘要

随着量子计算和信息技术的飞速进步，云量子机器学习作为一种新兴解决方案，为资源受限的用户通过远程量子服务器处理量子机器学习任务提供可能，同时确保了数据与模型的隐私保护。本文较全面地展示了该领域的最新研究动态，从量子内积和变分量子算法的基本理论出发，分析了基于量子内积的多种云量子机器学习和云变分量子算法的实施细节与应用实例。文章还讨论了当前技术面临的挑战，并对未来的研究方向提供了展望。

Abstract

With the rapid advancement of quantum computing and information technology

cloud-based quantum machine learning has emerged as a promising solution

enabling resource-constrained users to perform quantum machine learning tasks via remote quantum servers while ensuring privacy protection for both data and models. A relatively comprehensive overview of the latest developments in this field is provided

starting from the fundamental theories of quantum inner products and variational quantum algorithms. An analysis is conducted on the implementation details and application examples of various cloud-based quantum machine learning methods based on quantum inner products and cloud-based variational quantum algorithms. Additionally

the challenges faced by current technologies are discussed and insights into future research directions are offered.

关键词

Keywords

references

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