Home Mathematics 11 A review of different techniques and challenges of quantum computing in various applications
Chapter
Licensed
Unlicensed Requires Authentication

11 A review of different techniques and challenges of quantum computing in various applications

  • B. Aruna Devi , N. Alangudi Balaji and Mulugeta Tesema
Become an author with De Gruyter Brill

Abstract

Since its inception in the 1980s and early proof of principle for hardware in the 2000s, quantum computing has made tremendous strides. Despite the fact that the technology is still in the process of being developed, there are a number of people who believe that machines known as Noisy Intermediate Scale Quantum (NISQ) could soon outperform conventional computers. This is the result of the rapid development of hardware connected to quantum computing as well as the significant financial expenditures that have been made in many countries all over the world. In this chapter, a deep review of various applications of quantum computing is conducted.

Abstract

Since its inception in the 1980s and early proof of principle for hardware in the 2000s, quantum computing has made tremendous strides. Despite the fact that the technology is still in the process of being developed, there are a number of people who believe that machines known as Noisy Intermediate Scale Quantum (NISQ) could soon outperform conventional computers. This is the result of the rapid development of hardware connected to quantum computing as well as the significant financial expenditures that have been made in many countries all over the world. In this chapter, a deep review of various applications of quantum computing is conducted.

Chapters in this book

  1. Frontmatter I
  2. Preface V
  3. Contents VII
  4. Biographies XI
  5. List of contributors XIII
  6. 1 Optimizing the traffic flow in VANETs using deep quantum annealing 1
  7. 2 Quantum annealing-based routing in UAV network 13
  8. 3 Cyberbullying detection of social network tweets using quantum machine learning 25
  9. 4 AI-driven cybersecurity modeling using quantum computing for mitigation of attacks in IOT-SDN network 37
  10. 5 Machine learning-based quantum modeling to classify the traffic flow in smart cities 49
  11. 6 IoT attack detection using quantum deep learning in large-scale networks 67
  12. 7 Quantum transfer learning to detect passive attacks in SDN-IOT 79
  13. 8 Intrusion detection framework using quantum computing for mobile cloud computing 97
  14. 9 Fault-tolerant mechanism using intelligent quantum computing-based error reduction codes 109
  15. 10 Study of quantum computing for data analytics of predictive and prescriptive analytics models 121
  16. 11 A review of different techniques and challenges of quantum computing in various applications 147
  17. 12 Review and significance of cryptography and machine learning in quantum computing 159
  18. 13 An improved genetic quantum cryptography model for network communication 177
  19. 14 Code-based post-quantum cryptographic technique: digital signature 193
  20. 15 Post-quantum cryptography for the detection of injection attacks in small-scale networks 207
  21. 16 RSA security implementation in quantum computing for a higher resilience 219
  22. 17 Application of quantum computing for digital forensic investigation 231
  23. 18 Modern healthcare system: unveiling the possibility of quantum computing in medical and biomedical zones 249
  24. 19 Quantum computing-assisted machine learning to improve the prediction of cardiovascular disease in healthcare system 265
  25. 20 Mitigating the risk of quantum computing in cyber security era 283
  26. 21 IoMT-based data aggregation using quantum learning 301
  27. Index 319
Downloaded on 12.11.2025 from https://www.degruyterbrill.com/document/doi/10.1515/9783110798159-011/html
Scroll to top button