云量子机器学习研究进展

杨俊鸿; 王帮海; 钟志

doi:10.12052/gdutxb.240118

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云量子机器学习研究进展

计算机科学与技术 | 更新时间：2026-05-27

- 云量子机器学习研究进展
- Progress in Cloud-based Quantum Machine Learning
- 广东工业大学学报 2025年42卷第3期页码：1-11
- 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：
  
  杨俊鸿(1999–)，男，硕士研究生，主要研究方向为量子计算与量子信息，E-mail：junhong.yang@foxmail.com
  王帮海(1974–)，男，教授，博士，主要研究方向为量子计算与量子信息，E-mail：bhwang@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(62072119)
- DOI：10.12052/gdutxb.240118
  中图分类号： TP305;TP399
- 纸质出版：2025
- 稿件说明：
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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

DIVINCENZO D P. Quantum computation[J]. Science, 1995, 270(5234): 255-261.

ARUTE, F, ARYA, K. , BABBUSH, R, et al . Quantum supremacy using a programmable superconducting processor[J ] . Nature, 2019, 574(7779): 505-510.

DENG Y H, GU Y C LIU H L, et al . Gaussian boson sampling with pseudo-photon-number-resolving detectors and quantum computational advantage[J ] . Physical Review Letters, 2023, 131(15): 150601.

BIAMONTE J, WITTEK P, PANCOTTI N, et al . Quantum machine learning[J ] . Nature, 2017, 549(7671): 195-202.

REBENTROST P, MOHSENI M, LLOYD S. Quantum support vector machine for big data classification[J]. Physical Review Letters, 2014, 113(13): 130503.

WEI Y K, WANG Z H, LENG S P. Review of quantum reinforcement learning[J]. Journal of Guangzhou University, 2021, 20(1): 56-68.

赵翔. 基于量子计算的联邦学习模型: 202210115079[P]. 2024-10-10.

SOEPARNO H, PERBANGSA A S. Cloud quantum computing concept and development: a systematic literature review[J]. Procedia Computer Science, 2021, 179: 944-954.

ZHANG X, GAO F, QIN S J, et al . Current status and future development of quantum cry ptographic protocols[J ] . Strategic Study of CAE, 2022(004): 024.

LAU J W Z, CAI Z, THOMAS N, et al . NISQ computing: where are we and where do we go?[J ] . AAPPS Bulletin, 2022, 32(1): 27.

MOUSAVI M H, MADDAH-ALI M A, MIRMOHSENI M. Private inner product retrieval for distributed machine learning[C]//Proceedings of the 2019 IEEE International Symposium on Information Theory (ISIT). Paris: IEEE, 2019: 778-782.

LI J Y, ZHANG J, ZHANG J L, et al . Quantum KNN classification with K value selection and neighbor selection[J ] . IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2024, 43(5): 1332-1345.

CEREZO M, ARRASMITH A, BABBUSH R, et al . Variational quantum algorithms[J ] . Nature Reviews Physics, 2021, 3(9): 625-644.

TILLY J, CHEN H, CAO S X, et al . The variational quantum eigensolver: a review of methods and best practices[J ] . Physics Reports, 2022, 986: 1-128.

ZHOU L, WANG S T, CHOI S W, et al . Quantum approximate optimization algorithm: performance, mechanism, and implementation on near -term devices[J ] . Physical Review X, 2020, 10(2): 021067.

SCHULD M, SWEKE R, MEYER J J. Effect of data encoding on the expressive power of variational quantum-machine-learning models[J]. Physical Review A, 2021, 103(3): 032430.

WIERICHS D, IZAAC J, WANG C, et al . General parameter-shift rules for quantum gradients[J ] . Quantum, 2022, 6: 677.

SHENG Y B, ZHOU L. Distributed secure quantum machine learning[J]. Science Bulletin, 2017, 62(14): 1025-1029.

ZHOU X, QIU D. Blind quantum machine learning based on quantum circuit model[J]. Quantum Information Processing, 2021, 20: 363.

AMBAINIS A, MOSCA M, TAPP A, et al . Private quantum channels[C ] //Proceedings of the 41st Annual Symposium on Foundations of Computer Science. Washington: IEEE, 2000: 547-553.

LI C H, LI B N, AMER O, et al . Blind quantum machine learning with quantum bipartite correlator[J ] . Physical Review Letters, 2024, 133(12): 120602.

GRASSL M, LANGENBERG B, ROETTELER M, et al . Applying Grover’s algorithm to AES: quantum resource estimates[C ] //Proceedings of the International Workshop on Post-Quantum Cryptography. Fukuoka: Springer International Publishing, 2016: 29-43.

BERTHIAUME A, BRASSARD G. Oracle quantum computing[J]. Journal of Modern Optics, 1994, 41(12): 2521-2535.

LI W K, LU S R, DENG D L. Quantum federated learning through blind quantum computing[J]. Science China Physics, Mechanics Astronomy, 2021, 64: 100312.

BROADBENT A, FITZSIMONS J, KASHEFI E. Universal blind quantum computation[C]//Proceedings of the 50th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2009). Atlanta: IEEE, 2009: 517-526.

BRIEGEL H J, BROWNE D E, DÜR W, et al . Measurement-based quantum computation[J ] . Nature Physics, 2009, 5(1): 19-26.

MORIMAE T, FUJII K. Blind quantum computation protocol in which Alice only makes measurements[J]. Physical Review A, 2013, 87(5): 050301.

HOUSHMAND M, HOUSHMAND M, FITZSIMONS J F. Minimal qubit resources for the realization of measurement-based quantum computation[J]. Physical Review A, 2018, 98(1): 012318.

WANG Y X, QUAN J Y, LI Q. A delegated quantum approximate optimization algorithm[C]//Proceedings of the 2022 14th International Conference on Wireless Communications and Signal Processing (WCSP). Nanjing: IEEE, 2022: 804-808.

WANG B H, XU H R. Progress in blind quantum computation[J]. Journal of Guangdong University of Technology, 2015, 32(3): 51-55.

MA S Q, ZHU C H, LIU X C, et al . Universal blind quantum computation with improved brickwork states[J ] . Physical Review A, 2024, 109(1): 012606.

XU H R, WANG B H. Universal single-server blind quantum computation for classical clients[J]. Laser Physics Letters, 2021, 19(1): 015202.

MORIMAE T, FUJII K. Secure entanglement distillation for double-server blind quantum computation[J]. Physical Review Letters, 2013, 111(2): 020502.

LI Q, CHAN W H, ZHANG C, et al . Triple-server blind quantum computation using entanglement swapping[J ] . Physical Review A, 2014, 89(4): 040302.

ZHAO Q, LI C, LIU Y, et al . Fault-tolerant quantum error correction code preparation in UBQC[J ] . Quantum Information Processing, 2020, 19(8): 236.

FITZSIMONS J F, KASHEFI E. Unconditionally verifiable blind quantum computation[J]. Physical Review A, 2017, 96(1): 012303.

MORIMAE T. Verification for measurement-only blind quantum computing[J]. Physical Review A, 2014, 89(6): 060302.

ANDERS J, OI D K L, KASHEFI E, et al . Ancilla-driven universal quantum computation[J ] . Physical Review A, 2010, 82(2): 020301.

SHINGU Y, TAKEUCHI Y, ENDO S, et al . Variational secure cloud quantum computing[J ] . Physical Review A, 2022, 105(2): 022603.

LI Q, QUAN J Y, SHI J J, et al . Secure delegated variational quantum algorithms[J ] . IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2024, 43(10): 3129-3142.

DAWSON C M, NIELSEN M A. The Solovay-kitaev algorithm[J]. Quantum Information and Computation, 2006, 6(1): 81-95.

SUN W L, CHANG Y, WANG D C, et al . Delegated quantum neural networks for encrypted data[J ] . Physica Scripta, 2024, 99(5): 055102.

WANG Z P, LI J Y, HU Z R, et al . Qumos: a framework for preserving security of quantum machine learning model[C ] //Proceedings of the 2023 IEEE International Conference on Quantum Computing and Engineering (QCE). Bellevue: IEEE, 2023: 1089-1097.

GUPTA R, SAXENA D, GUPTA I, et al . Quantum machine learning driven malicious user prediction for cloud network communications[J ] . IEEE Networking Letters, 2022, 4(4): 174-178.

WIEBE N, KAPOOR A, SVORE K M. Quantum deep learning[J]. Quantum Information Computation, 2016, 16(7-8): 541-587.

MANTILLA CALDERÓN L C. Measurement-based quantum machine learning[D]. Vancouver: University of British Columbia, 2023.

LI J, LIU Q, WANG L, et al . Research progress and key technology analysis of quantum network systems[J ] . Strategic Study of CAE, 2024, 25(6): 80-95.

WANG S B, ZHANG W, LIU C, et al . Research progress of distributed quantum computing[J ] . Chinese Journal of Quantum Electronics, 2024, 41(1): 1-25.

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