Quantum-inspired support vector machine
Open accessAbstract
Support vector machine (SVM) is a particularly powerful and flexible supervised learning model that analyzes data for both classification and regression, whose usual algorithm complexity scales polynomially with the dimension of data space and the number of data points. To tackle the big data challenge, a quantum SVM algorithm was proposed, which is claimed to achieve exponential speedup for least squares SVM (LS-SVM). Here, inspired by the quantum SVM algorithm, we present a quantum-inspired classical algorithm for LS-SVM. In our approach, an improved fast sampling technique, namely indirect sampling, is proposed for sampling the kernel matrix and classifying. We first consider the LS-SVM with a linear kernel, and then discuss the generalization of our method to nonlinear kernels. Theoretical analysis shows our algorithm can make classification with arbitrary success probability in logarithmic runtime of both the dimension of data space and the number of data points for low rank, low condition number, and high dimensional data matrix, matching the runtime of the quantum SVM.
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There have been attempts to prove blind quantum computing through experimentation, in which quantum servers are unaware of the inputs, delegations, calculations, or outputs.
“There have been attempts to prove blind quantum computing through experimentation, in which quantum servers are unaware of the inputs, delegations, calculations, or outputs [20].”
From Quantum Computing: Vision and Challenges (2025) · cites this paper as [20]
The References (p. 12) identify Huang et al. (2017), not this support-vector-machine paper, as the work titled “Experimental blind quantum computing for a classical client.” The Introduction (p. 1) does not discuss blind computing, and our copy does not describe what a quantum server can or cannot know. (LLM, conf=0.78) It matches “Experimental blind quantum computing for a classical client” (2017) instead.
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References 39 · Cited by 186
Research works printed but not linked: 6
- [1][1] H.-L. Huang, D. Wu, D. Fan, and X. Zhu, 'Superconducting quantum computing: a review,' Science China Information Sciences , vol. 63, no. 180501, 2020. -
- [10][10] H.-L. Huang, Y.-W. Zhao, T. Li, F.-G. Li, Y.-T. Du, X.-Q. Fu, S. Zhang, X. Wang, and W.-S. Bao, 'Homomorphic encryption experiments on ibm's cloud quantum computing platform,' Frontiers of Physics , vol. 12, no. 1, p. 120305, 2017. [Online]. Available: https://link.springer.com/article/10.1007/s11467-016-0643-9 - · DOI
- [11][11] H.-L. Huang, Y. Du, M. Gong, Y. Zhao, Y. Wu, C. Wang, S. Li, F. Liang, J. Lin, Y. Xu et al. , 'Experimental quantum generative adversarial networks for image generation,' arXiv:2010.06201 , 2020. - · arXiv
- [12][12] H.-L. Huang, A. K. Goswami, W.-S. Bao, and P. K. Panigrahi, 'Demonstration of essentiality of entanglement in a deutsch-like quantum algorithm,' SCIENCE CHINA Physics, Mechanics & Astronomy , vol. 61, no. 060311, 2018. -
- [13][13] H.-L. Huang, M. Naro˙ zniak, F. Liang, Y. Zhao, A. D. Castellano, M. Gong, Y. Wu, S. Wang, J. Lin, Y. Xu et al. , 'Emulating quantum teleportation of a majorana zero mode qubit,' Physical Review Letters , vol. 126, no. 9, p. 090502, 2021. -
- [22][22] N.-H. Chia, H.-H. Lin, and C. Wang, 'Quantum-inspired sublinear classical algorithms for solving low-rank linear systems,' arXiv preprint , Nov. 2018. [Online]. Available: https://arxiv.org/abs/1811.04852 - · arXiv
- Authors
- Ding, C., Bao, T.-Y., & Huang, H.-L.
- Affiliations
- University of Science and Technology of China (CN)
- University of Oxford (GB)
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2022-12
- Volume
- 33
- Issue
- 12
- Pages
- 7210-7222
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W3169581362
- Cited by
- 186
- References
- 39
- Citations by year
- 2026: 19 · 2025: 45 · 2024: 45 · 2023: 33 · 2022: 25 · 2021: 11 · 2020: 8
- FWCI
- 9.98
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Neural Networks and Learning Systems (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Open Research Fund from State Key Laboratory of High Performance Computing of China (201901-01)
- National Natural Science Foundation of China (11905294)
- China Postdoctoral Science Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Keywords
- quantum-inspired algorithms, support vector machine, least squares SVM, indirect sampling, quantum machine learning, big data classification, linear kernel, nonlinear kernels, low-rank matrices
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