Industrial anomaly detection; machine learning; quantum control; quantum semantic communications; Anomaly detection; Cybe-physical systems; Cyber-physical systems; Machine-learning; Quantum control; Quantum semantic communication; Quantum semantics; Semantic communication; Systems networks; Control and Systems Engineering; Computer Science Applications; Computer Networks and Communications
Abstract :
[en] Computing-intensive semantic communication emphasizes context, enabling the extraction of task-specific semantics from the source data and the reconstruction of the intended meaning at the destination. In industrial cyber-physical systems (CPSs), this approach can optimize automation processes while minimizing communication overhead with efficient bandwidth use in environments where machines, sensors, and controllers must communicate frequently. By integrating quantum communication with computing-empowered semantic methods, we can achieve unprecedented efficiency and security in task-oriented data transmission, effectively safeguarding against eavesdropping and other attacks. This paper presents a controlled quantum semantic communication (QSC) framework that leverages semantic extraction for anomaly detection in industrial CPS networks and employs controlled quantum communication to send the data securely with high semantic fidelity. A machine learning model extracts semantic information from images as the hull point data representing defective regions as pixel points. This data is then transmitted with high fidelity using quantum communication with controlled quantum state preparation. We use discrete- and continuous-variable states to simulate quantum binary phaseshift keying (BPSK) and M-ary pulse position modulation (M-PPM), respectively. At the receiver, these quantum states are measured using optimal quantum decision-making and converted back into the hull point data, thereby generating the anomaly map. This map is overlaid on a template image to highlight defect positions, which can be used for industrial quality control. Furthermore, we simulate the controlled QSC framework (BPSK and M-PPM) across a diverse set of anomaly detection examples and evaluate the QSC performance in industrial CPS networks.
Disciplines :
Computer science
Author, co-author :
Rizvi, Syed Muhammad Abuzar; Kyung Hee University, Department of Electronics and Information Convergence Engineering, Yongin-si, South Korea
Khalid, Uman; Kyung Hee University, Department of Electronics and Information Convergence Engineering, Yongin-si, South Korea
CHATZINOTAS, Symeon ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Duong, Trung Q.; Memorial University, Faculty of Engineering and Applied Science, St. John's, Canada ; Queen's University Belfast, School of Electronics, Electrical Engineering and Computer Science, Belfast, United Kingdom
Shin, Hyundong; Kyung Hee University, Department of Electronics and Information Convergence Engineering, Yongin-si, South Korea
External co-authors :
yes
Language :
English
Title :
Controlled Quantum Semantic Communication for Industrial CPS Networks
Publication date :
15 July 2025
Journal title :
IEEE Transactions on Network Science and Engineering
A. Colombo, S. Karnouskos, Y. Shi, and S. Yin, “Industrial cyber-physical systems: A backbone of the fourth industrial revolution,” IEEE Ind. Electron. Mag., vol. 11, no. 1, pp. 6–16, Mar. 2017.
F. Tao, H. Zhang, and A. Liu, “Digital twins and cyber-physical systems toward smart manufacturing and Industry 4.0,” IEEE Trans. Ind. Informat., vol. 15, no. 4, pp. 2405–2415, Apr. 2019.
B. Chen, J. Wan, L. Shu, P. Li, M. Mukherjee, and B. Yin, “Smart factory of Industry 4.0: Key technologies, application case, and challenges,” IEEE Access, vol. 6, pp. 6505–6519, Dec. 2017.
A. Mostaani, T. X. Vu, S. K. Sharma, V.-D. Nguyen, Q. Liao, and S. Chatzinotas, “Task-oriented communication design in cyber-physical systems: A survey on theory and applications,” IEEE Access, vol. 10, pp. 133 842–133 868, Dec. 2022.
L. Li, K. Ota, and M. Dong, “Deep learning for smart Industry: Efficient manufacture inspection system with fog computing,” IEEE Trans. Ind. Informat., vol. 14, no. 10, pp. 4665–4673, Oct. 2018.
J. Wan, J. Yang, Z. Wang, and Q. Hua, “Artificial intelligence for cloud-assisted smart factory,” IEEE Access, vol. 6, pp. 55 419–55 430, Sep. 2018.
J. Wang, L. Wu, K.-K. R. Choo, and D. He, “Blockchain-based anonymous authentication with key management for smart grid edge computing infrastructure,” IEEE Trans. Ind. Informat., vol. 16, no. 3, pp. 1984–1992, Mar. 2020.
J. Wan, S. Tang, H. Yan, D. Li, and S. Wang, “Software-defined industrial Internet of Things in the context of Industry 4.0,” IEEE Sensors J., vol. 16, no. 20, pp. 7373–7380, Oct. 2016.
A. M. Ramly, N. F. Abdullah, and R. Nordin, “Cross-layer design and performance analysis for ultra-reliable factory of the future based on 5G mobile networks,” IEEE Access, vol. 9, p. 68161–68175, May 2021.
Y. Yang, C. Guo, F. Liu, C. Liu, L. Sun, Q. Sun, and J. Chen, “Semantic communications with artificial intelligence tasks: Reducing bandwidth requirements and improving artificial intelligence task performance,” IEEE Ind. Electron. Mag., pp. 2–11, May 2022.
U. Khalid, M. S. Ulum, A. Farooq, T. Q. Duong, O. A. Dobre, and H. Shin, “Quantum semantic communications for Metaverse: Principles and challenges,” IEEE Wireless Commun., vol. 30, no. 4, pp. 26–36, Aug. 2023.
H. Xie and Z. Qin, “A lite distributed semantic communication system for Internet of Things,” IEEE J. Sel. Areas Commun., vol. 39, no. 1, pp. 142–153, Jan. 2021.
S. Tariq, U. Khalid, B. E. Arfeto, T. Q. Duong, and H. Shin, “Integrating sustainable big AI: Quantum anonymous semantic broadcast,” IEEE Wireless Commun., vol. 31, no. 3, pp. 86–99, Jun. 2024.
F. Zaman, A. Farooq, M. A. Ullah, H. Jung, H. Shin, and M. Z. Win, “Quantum machine intelligence for 6G URLLC,” IEEE Wireless Commun., vol. 30, no. 2, pp. 22–30, Apr. 2023.
U. Khalid, J. Ur Rehman, S. N. Paing, H. Jung, T. Q. Duong, and H. Shin, “Quantum network engineering in the NISQ age: Principles, missions, and challenges,” IEEE Netw., vol. 38, no. 1, pp. 112–123, Jan. 2024.
G. Rathee, A. Sharma, R. Kumar, and R. Iqbal, “A secure communicating things network framework for industrial IoT using blockchain technology,” Ad Hoc Netw., vol. 94, p. 101933, Nov. 2019.
M. R. Asghar, Q. Hu, and S. Zeadally, “Cybersecurity in industrial control systems: Issues, technologies, and challenges,” Comput. Netw., vol. 165, p. 106946, Dec. 2019.
H. Kayan, M. Nunes, O. Rana, P. Burnap, and C. Perera, “Cybersecurity of industrial cyber-physical systems: A review,” ACM Comput. Surv., vol. 54, no. 11s, pp. 1–35, Sep. 2022.
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, “Quantum machine learning,” Nature, vol. 549, no. 7671, pp. 195–202, Sep. 2017.
S. Tariq, B. E. Arfeto, U. Khalid, S. Kim, T. Q. Duong, and H. Shin, “Deep quantum-transformer networks for multimodal beam prediction in ISAC systems,” IEEE Internet Things J., vol. 11, no. 18, pp. 29 387–29 401, Sep. 2024.
A. Bayerstadler, G. Becquin, J. Binder, T. Botter, and et al., “Industry quantum computing applications,” EPJ Quantum Technol., vol. 8, no. 1, p. 25, Nov. 2021.
A. Perdomo-Ortiz, A. Feldman, A. Ozaeta, and et al., “Readiness of quantum optimization machines for industrial applications,” Phys. Rev. A, vol. 12, no. 1, p. 014004, Jul. 2019.
M. S. Ulum, U. Khalid, J. W. Setiawan, T. Q. Duong, M. Z. Win, and H. Shin, “Variational anonymous quantum sensing,” IEEE J. Sel. Areas Commun., vol. 42, no. 9, pp. 2275–2291, Sep. 2024.
S. N. Paing, J. W. Setiawan, M. A. Ullah, F. Zaman, T. Q. Duong, O. A. Dobre, and H. Shin, “Counterfactual quantum Byzantine consensus for human-centric Metaverse,” IEEE J. Sel. Areas Commun., vol. 42, no. 4, pp. 905–918, Apr. 2024.
N. Gisin and R. Thew, “Quantum communication,” Nat. Photonics, vol. 1, no. 3, pp. 165–171, Mar. 2007.
D. Cozzolino, B. D. Lio, D. Bacco, and L. K. Oxenløwe, “High-dimensional quantum communication: Benefits, progress, and future challenges,” Adv. Quantum Technol., vol. 2, no. 12, p. 1900038, Oct. 2019.
S. N. Paing, J. W. Setiawan, T. Q. Duong, D. Niyato, M. Z. Win, and H. Shin, “Quantum anonymous networking: A quantum leap in privacy,” IEEE Netw., vol. 38, no. 5, pp. 131–145, Sep. 2024.
M. M. Wilde, Quantum Information Theory, 2nd ed. Cambridge, U.K.: Cambridge Univ. Press, 2017.
F. Zaman, S. N. Paing, A. Farooq, H. Shin, and M. Z. Win, “Concealed quantum telecomputation for anonymous 6G URLLC networks,” IEEE J. Sel. Areas Commun., vol. 41, no. 7, pp. 2278–2296, Jul. 2023.
W. Yang, H. Du, Z. Liew, W. Y. B. Lim, Z. Xiong, D. Niyato, X. Chi, X. S. Shen, and C. Miao, “Semantic communications for future internet: Fundamentals, applications, and challenges,” IEEE Commun. Surveys Tuts., vol. 25, no. 1, pp. 213–250, Nov. 2023.
F. Zaman, U. Khalid, T. Q. Duong, H. Shin, and M. Z. Win, “Quantum full-duplex communication,” IEEE J. Sel. Areas Commun., vol. 41, no. 9, pp. 2966–2980, Sep. 2023.
L. Oleynik, J. U. Rehman, H. Al-Hraishawi, and S. Chatzinotas, “Variational estimation of optimal signal states for quantum channels,” IEEE Trans. Quantum Eng., vol. 5, pp. 1–8, Apr. 2024.
G. Cariolaro, Quantum Communications, 1st ed. Cham, Switzerland.: Springer, 2015.
C. W. Helstrom, “Quantum detection and estimation theory,” J. Stat. Phys., vol. 1, pp. 231–252, 1969.
J. Yu, Y. Zheng, X. Wang, W. Li, Y. Wu, R. Zhao, and L. Wu, “Fastflow: Unsupervised anomaly detection and localization via 2D normalizing flows,” arXiv:2111.07677, Nov. 2021.
T. Caneva, T. Calarco, and S. Montangero, “Chopped random-basis quantum optimization,” Phys. Rev. A, vol. 84, no. 2, p. 022326, Aug. 2011.
P. de Fouquieres, S. G. Schirmer, S. J. Glaser, and I. Kuprov, “Second order gradient ascent pulse engineering,” J. Magn. Reson., vol. 212, no. 2, pp. 412–417, Aug. 2011.
G. Jäger, D. M. Reich, M. H. Goerz, C. P. Koch, and U. Hohenester, “Optimal quantum control of Bose-Einstein condensates in magnetic microtraps: Comparison of gradient-ascent-pulse-engineering and Krotov optimization schemes,” Phys. Rev. A, vol. 90, no. 3, p. 033628, Sep. 2014.
M. Bukov, A. G. R. Day, D. Sels, P. Weinberg, A. Polkovnikov, and P. Mehta, “Reinforcement learning in different phases of quantum control,” Phys. Rev. X, vol. 8, no. 3, p. 031086, Sep. 2018.
M. Y. Niu, S. Boixo, V. N. Smelyanskiy, and H. Neven, “Universal quantum control through deep reinforcement learning,” npj Quantum Inform., vol. 5, no. 1, p. 33, Apr. 2019.
L. Hanschke, K. A. Fischer, S. Appel, and et al., “Quantum dot single-photon sources with ultra-low multi-photon probability,” npj Quantum Inform., vol. 4, p. 43, Sep. 2018.
C. E. Shannon, “A mathematical theory of communication,” BSTJ, vol. 27, pp. 623–656, Jul. 1948.
M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information. Cambridge, UK: Cambridge University Press, 2000.
V. Scarani, H. Bechmann-Pasquinucci, N. J. Cerf, M. Dušek, N. Lütkenhaus, and M. Peev, “The security of practical quantum key distribution,” Rev. Mod. Phys., vol. 81, no. 3, pp. 1301–1350, Sep 2009.
A. Unnikrishnan, I. J. MacFarlane, R. Yi, E. Diamanti, D. Markham, and I. Kerenidis, “Anonymity for practical quantum networks,” Phys. Rev. Lett., vol. 122, no. 24, p. 240501, Jun. 2019.
M. Chehimi, C. Chaccour, and W. Saad, “Quantum semantic communications: An unexplored avenue for contextual networking,” arXiv:2205.02422, May 2022.
M. Chehimi, C. Chaccour, C. K. Thomas, and W. Saad, “Quantum semantic communications for resource-efficient quantum networking,” IEEE Wireless Commun. Lett., vol. 28, no. 4, pp. 803–807, Apr. 2024.
N. Nunavath, E. C. Strinati, R. Bassoli, and F. H. P. Fitzek, “Pragmatic semantic communication through quantum channel,” in Proc. IEEE 3rd Int. Conf. 6G Networking (6GNet), Oct. 2024, pp. 189–195.
U. Khalid, M. S. Ulum, A. Farooq, T. Q. Duong, O. A. Dobre, and H. Shin, “Quantum semantic communications for Metaverse: Principles and challenges,” IEEE Wireless Commun., vol. 30, no. 4, pp. 26–33, 2023.
J. Preskill, “Quantum computing in the NISQ era and beyond,” Quantum, vol. 2, p. 79, Aug. 2018.
Z.-Q. Zhao, P. Zheng, S.-T. Xu, and X. Wu, “Object detection with deep learning: A review,” IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 11, pp. 3212–3232, Nov. 2019.
K. Imoto, T. Nakai, T. Ike, K. Haruki, and Y. Sato, “A CNN-based transfer learning method for defect classification in semiconductor manufacturing,” IEEE Trans. Semicond. Manuf., vol. 32, no. 4, pp. 455–459, Apr. 2019.
Z. Huang, J. Zhu, J. Lei, X. Li, and F. Tian, “Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations,” J. Intell. Manuf., vol. 31, no. 4, pp. 953–966, 2020.
H. Huang et al., “Real-time fault detection for IIoT facilities using GBRBM-based DNN,” IEEE Internet Things J., vol. 7, no. 7, pp. 5713–5722, Jul. 2020.
Y. Huang, J. Jing, and Z. Wang, “Fabric defect segmentation method based on deep learning,” IEEE Trans. Instrum. Meas., vol. 70, pp. 1–15, Jan. 2021.
T. Wang, Y. Yao, Y. Chen, M. Zhang, F. Tao, and H. Snoussi, “Auto-sorting system toward smart factory based on deep learning for image segmentation,” IEEE Sensors J., vol. 18, no. 20, pp. 8493–8501, Oct. 2018.
M. Ghahramani, Y. Qiao, M. Zhou, A. O’Hagan, and J. Sweeney, “AI-based modeling and data-driven evaluation for smart manufacturing processes,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1026–1037, Jul. 2020.
C. Dai, X. Liu, H. Xu, L. T. Yang, and J. Deen, “Hybrid deep model for human behavior understanding on industrial Internet of Video Things,” IEEE Trans. Ind. Informat., vol. 18, no. 10, pp. 7000–7008, Oct. 2022.
M. de Berg, M. van Kreveld, M. Overmars, and O. Schwarzkopf, Computational Geometry: Algorithms and Applications, 2nd ed. Berlin, Germany: Springer, 2000.
C. H. Bennett and G. Brassard, “Quantum cryptography: Public key distribution and coin tossing,” Theor. Comput. Sci., vol. 560, pp. 7–11, Dec. 2014.
W. K. Wootters and W. H. Zurek, “A single quantum cannot be cloned,” Nature, vol. 299, no. 5886, pp. 802–803, Oct 1982.
V. Lipinska, G. Murta, and S. Wehner, “Anonymous transmission in a noisy quantum network using the W state,” Phys. Rev. A, vol. 98, no. 5, p. 052320, Nov. 2018.
W. Yang, L. Huang, and F. Song, “Privacy preserving quantum anonymous transmission via entanglement relay,” Sci. Rep., vol. 6, no. 1, p. 26762, Jun. 2016.
A. Khan, U. Khalid, J. Ur Rehman, and H. Shin, “Quantum anonymous private information retrieval for distributed networks,” IEEE Trans. Commun., vol. 70, no. 6, pp. 4026–4037, Apr. 2022.
R.-H. Shi and X.-Q. Fang, “Anonymous classical message transmission through various quantum networks,” IEEE Trans. Netw. Sci. Eng., vol. 11, no. 3, pp. 2901–2913, May 2024.
R. Kaewpuang, M. Xu, W. Y. B. Lim, D. Niyato, H. Yu, J. Kang, and X. Shen, “Cooperative resource management in quantum key distribution QKD networks for semantic communication,” IEEE Internet Things J., vol. 11, no. 5, pp. 4454–4469, Feb. 2023.
D. Bouwmeester, J.-W. Pan, K. Mattle, M. Eibl, H. Weinfurter, and A. Zeilinger, “Experimental quantum teleportation,” Nature, vol. 390, pp. 575–579, Dec. 1997.
S. Olmschenk, D. N. Matsukevich, P. Maunz, D. Hayes, L. Duan, and C. Monroe, “Quantum teleportation between distant matter qubits,” Science, vol. 323, pp. 486–489, Jan. 2009.
A. Furusawa, J. L. Sørensen, S. L. Braunstein, C. A. Fuchs, H. J. Kimble, and E. S. Polzik, “Unconditional quantum teleportation,” Science, vol. 282, pp. 706–709, Oct. 1998.
F. Grosshans and P. Grangier, “Continuous variable quantum cryptography using coherent states,” Phys. Rev. Lett., vol. 88, no. 5, p. 057902, Jan. 2002.
S. Pirandola, U. L. Andersen, L. Banchi, M. Berta, D. Bunandar, R. Colbeck, D. Englund, T. Gehring, C. Lupo, C. Ottaviani et al., “Advances in quantum cryptography,” Adv. Opt. Photonics, vol. 12, pp. 1012–1236, December 2020.
N. J. Cerf, G. Leuchs, and E. S. Polzik, Quantum Information with Continuous Variables of Atoms and Light. London, U.K.: Imperial College Press, 2007.
X.-M. Zhang, Z. Wei, R. Asad, X.-C. Yang, and X. Wang, “When does reinforcement learning stand out in quantum control? A comparative study on state preparation,” npj Quantum Inform., vol. 5, no. 1, p. 85, Nov. 2019.
D. M. Reich, M. Ndong, and C. P. Koch, “Monotonically convergent optimization in quantum control using krotov’s method,” J. Chem. Phys., vol. 136, no. 10, Mar. 2012.
S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, and U. Genc, “Anomalib: A deep learning library for anomaly detection,” in Proc. IEEE Int. Conf. Image Process. (ICIP), Bordeaux, France, Oct. 2022, pp. 1706–1710.
J. Weinbub and D. Ferry, “Recent advances in Wigner function approaches,” Appl. Phys. Rev., vol. 5, no. 4, p. 041104, Oct. 2018.
M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (VOC) challenge,” Int. J. Comput. Vis., vol. 88, no. 2, pp. 303–338, Sep. 2009.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, Apr. 2004.
M. P. Dubuisson and A. K. Jain, “A modified Hausdorff distance for object matching,” in Proc. IEEE Int. Conf. Pattern Recognit. (ICPR), Jerusalem, Israel, Oct. 1994, pp. 566–568.
E. del Valle, S. Zippilli, F. P. Laussy, A. Gonzalez-Tudela, G. Morigi,, and C. Tejedor, “Two-photon lasing by a single quantum dot in a high-Q microcavity,” Phys. Rev. B, vol. 81, p. 035302, Jan. 2010.
A. Vivas-Viaña and C. S. Muñoz, “Two-photon resonance fluorescence of two interacting nonidentical quantum emitters,” Phys. Rev. Res., vol. 3, p. 033136, Aug. 2021.
K. A. Fischer, R. Trivedi, and D. Lukin, “Particle emission from open quantum systems,” Phys. Rev. A, vol. 98, p. 023853, Aug. 2018.
H. Carmichael, An Open Systems Approach to Quantum Optics, 1st ed. Berlin, Heidelberg: Springer-Verlag, 1993.
J. C. Lagarias, J. A. Reeds, M. H. Wright, and P. E. Wright, “Convergence properties of the Nelder-Mead simplex method in low dimensions,” SIAM J. Optim., vol. 9, no. 1, pp. 112–147, 1998.
