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Cited by 0Quantum Computing Algorithms and ArchitectureNeural Networks and ApplicationsAdversarial Robustness in Machine Learning

The DOI matches its Crossref record, with a small difference in title or year; 13 of the 13 references it cites were found in a registry or an index.

Quantum-inspired weight-constrained neural network: Reducing variable numbers by 100 × compared to standard neural networks

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Li, Shaozhi, Salek, M Sabbir, Chowdhury, Mashrur, Wang, Yao·2026·Physical Review Research
DOI 10.1103/wb1b-ntpc

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Abstract

Although quantum machine learning has shown great promise, the practical application of quantum computers remains constrained in the noisy intermediate-scale quantum era. To take advantage of quantum machine learning, we investigate the underlying mathematical principles of these quantum models and find that the quantum neural network with amplitude encoding is equivalent to a weight-constrained neural network. Motivated by this discovery, we develop a classical weight-constrained neural network. We find that this approach can reduce the number of variables in a classical neural network by a factor of 135 while preserving its accuracy. In addition, we develop a dropout method to enhance the robustness of quantum machine learning models, which are highly susceptible to adversarial attacks. This technique can also be applied to improve the adversarial robustness of the classical weight-constrained neural network, which is essential for industry applications, such as self-driving vehicles. Our work offers an approach to reduce the complexity of large classical neural networks, addressing a critical challenge in machine learning.

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Research works printed but not linked: 46

  1. 10.1038/s41591-023-02448-8 · DOI · Paper page
  2. 10.1109/JBHI.2023.3316750 · DOI
  3. J. Devlin, 2019, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota, USA
  4. Z. Allen-Zhu, 2019, Advances in Neural Information Processing Systems
  5. D. Erhan, 2009, Proceedings of the 12th International Conference on Artificial Intelligence and Statistics
  6. T. Bachlechner, 2021, Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence
  7. 10.1038/s41586-019-1666-5 · DOI · Paper page
  8. 10.1103/PRXQuantum.3.030101 · DOI
  9. 10.1038/s41467-024-46959-5 · DOI
  10. 10.1038/s43588-022-00311-3 · DOI
  11. 10.1103/PhysRevX.4.031002 · DOI · Paper page
  12. 10.1103/PhysRevLett.113.130503 · DOI · Paper page
  13. 10.1038/nature23474 · DOI · Paper page
  14. 10.1098/rspa.2017.0551 · DOI · Paper page
  15. 10.1038/s41534-020-0272-6 · DOI
  16. 10.1038/s41534-019-0223-2 · DOI · Paper page
  17. 10.1038/s41586-019-0980-2 · DOI · Paper page
  18. 10.1038/s41534-021-00503-1 · DOI
  19. 10.1103/PhysRevA.107.022402 · DOI
  20. 10.1103/PhysRevA.107.062417 · DOI
  21. 10.1088/2058-9565/aabd98 · DOI
  22. 10.1103/PhysRevX.12.031010 · DOI
  23. 10.1109/TQE.2022.3176806 · DOI
  24. 10.1103/RevModPhys.87.307 · DOI · Paper page
  25. 10.1103/PhysRevLett.84.2525 · DOI · Paper page
  26. E. Tang, 2019, Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing
  27. 10.1038/s41534-021-00443-w · DOI
  28. 10.1109/OJVT.2022.3202876 · DOI · Paper page
  29. 10.1109/TNNLS.2021.3084467 · DOI · Paper page
  30. 10.1103/PhysRevA.104.042408 · DOI
  31. 10.1109/ACCESS.2019.2904624 · DOI
  32. 10.1016/j.bspc.2023.105560 · DOI
  33. K. Sun, 2018, Proceedings of the British Machine Vision Conference 2018 (BMVC 2018), Newcastle upon Tyne, UK
  34. 10.1103/PhysRevA.101.062331 · DOI
  35. 10.1093/nsr/nwab011 · DOI
  36. 10.1103/PhysRevResearch.3.023153 · DOI
  37. 10.1103/PhysRevResearch.2.033212 · DOI
  38. 10.1109/ACCESS.2020.3037705 · DOI
  39. Y. Deng, 2020, Proceedings of the 2020 IEEE International Conference on Pervasive Computing and Communications, PerCom, Austin, TX
  40. I. J. Goodfellow, 2015, Proceedings of the 3rd International Conference on Learning Representations
  41. N. Papernot, 2016, Proceedings of the 2016 IEEE Symposium on Security and Privacy (SP), San Jose, California
  42. E. Wong, 2018, Proceed- ings of the 35th International Conference on Machine Learning
  43. 10.22331/q-2024-02-22-1265 · DOI
  44. 10.1162/neco.1996.8.7.1341 · DOI
  45. 10.1109/TITS.2012.2209421 · DOI
  46. C. Ertler, 2020, Computer Vision ECCV 2020

Metadata

Authors
Li, S., Salek, M. S., Chowdhury, M., & Wang, Y.
Affiliations
  • Clemson University (US)
  • Emory University (US)
ORCID
Venue
Physical Review Research
Publisher
American Physical Society (APS)
Published
2026-02-09
Volume
8
Issue
1
ISSN
2643-1564 (ISSN-L)
DOI
10.1103/wb1b-ntpc
OpenAlex
W4405902766
Cited by
0
References
13
FWCI
0.00
Type
journal article
Language
en
Open access
Licence
Locations
  • Physical Review Research (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
  • U.S. Department of Energy (DE-SC0024524)
  • Clemson University
  • National Center for Transportation Cybersecurity and Resiliency
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
Keywords
quantum neural networks, amplitude encoding, quantum machine learning, variable reduction, dropout, adversarial robustness, noisy intermediate-scale quantum computing

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