10 Study of quantum computing for data analytics of predictive and prescriptive analytics models
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Kausha Kishor
Abstract
People now also earn money within a limited amount of time by spending it. This is going to happen when you make the correct decisions. In the coming years, quantum computing is expected to discover new patterns and solutions, as well as provide results faster and in a more energy- and cost-efficient fashion in selected analytics use case scenarios, but only if an integration of technology-affine business analysts and imposed researchers can effectively use and extend unique business and technology aware methods to “quantize” the business problem by developing a user accessible workflow. This quantification comprises two key problems to be overcome in terms of business needs on one side and R&D feasibility on the other. Despite difficulties in scaling quantum systems and integrating them into commercial data pipelines, the sector is rapidly approaching enterprise readiness. This chapter details and summarizes the existing language on prescriptive analytics, highlighting current problems and exploring possible solutions.
Abstract
People now also earn money within a limited amount of time by spending it. This is going to happen when you make the correct decisions. In the coming years, quantum computing is expected to discover new patterns and solutions, as well as provide results faster and in a more energy- and cost-efficient fashion in selected analytics use case scenarios, but only if an integration of technology-affine business analysts and imposed researchers can effectively use and extend unique business and technology aware methods to “quantize” the business problem by developing a user accessible workflow. This quantification comprises two key problems to be overcome in terms of business needs on one side and R&D feasibility on the other. Despite difficulties in scaling quantum systems and integrating them into commercial data pipelines, the sector is rapidly approaching enterprise readiness. This chapter details and summarizes the existing language on prescriptive analytics, highlighting current problems and exploring possible solutions.
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- Biographies XI
- List of contributors XIII
- 1 Optimizing the traffic flow in VANETs using deep quantum annealing 1
- 2 Quantum annealing-based routing in UAV network 13
- 3 Cyberbullying detection of social network tweets using quantum machine learning 25
- 4 AI-driven cybersecurity modeling using quantum computing for mitigation of attacks in IOT-SDN network 37
- 5 Machine learning-based quantum modeling to classify the traffic flow in smart cities 49
- 6 IoT attack detection using quantum deep learning in large-scale networks 67
- 7 Quantum transfer learning to detect passive attacks in SDN-IOT 79
- 8 Intrusion detection framework using quantum computing for mobile cloud computing 97
- 9 Fault-tolerant mechanism using intelligent quantum computing-based error reduction codes 109
- 10 Study of quantum computing for data analytics of predictive and prescriptive analytics models 121
- 11 A review of different techniques and challenges of quantum computing in various applications 147
- 12 Review and significance of cryptography and machine learning in quantum computing 159
- 13 An improved genetic quantum cryptography model for network communication 177
- 14 Code-based post-quantum cryptographic technique: digital signature 193
- 15 Post-quantum cryptography for the detection of injection attacks in small-scale networks 207
- 16 RSA security implementation in quantum computing for a higher resilience 219
- 17 Application of quantum computing for digital forensic investigation 231
- 18 Modern healthcare system: unveiling the possibility of quantum computing in medical and biomedical zones 249
- 19 Quantum computing-assisted machine learning to improve the prediction of cardiovascular disease in healthcare system 265
- 20 Mitigating the risk of quantum computing in cyber security era 283
- 21 IoMT-based data aggregation using quantum learning 301
- Index 319
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- Biographies XI
- List of contributors XIII
- 1 Optimizing the traffic flow in VANETs using deep quantum annealing 1
- 2 Quantum annealing-based routing in UAV network 13
- 3 Cyberbullying detection of social network tweets using quantum machine learning 25
- 4 AI-driven cybersecurity modeling using quantum computing for mitigation of attacks in IOT-SDN network 37
- 5 Machine learning-based quantum modeling to classify the traffic flow in smart cities 49
- 6 IoT attack detection using quantum deep learning in large-scale networks 67
- 7 Quantum transfer learning to detect passive attacks in SDN-IOT 79
- 8 Intrusion detection framework using quantum computing for mobile cloud computing 97
- 9 Fault-tolerant mechanism using intelligent quantum computing-based error reduction codes 109
- 10 Study of quantum computing for data analytics of predictive and prescriptive analytics models 121
- 11 A review of different techniques and challenges of quantum computing in various applications 147
- 12 Review and significance of cryptography and machine learning in quantum computing 159
- 13 An improved genetic quantum cryptography model for network communication 177
- 14 Code-based post-quantum cryptographic technique: digital signature 193
- 15 Post-quantum cryptography for the detection of injection attacks in small-scale networks 207
- 16 RSA security implementation in quantum computing for a higher resilience 219
- 17 Application of quantum computing for digital forensic investigation 231
- 18 Modern healthcare system: unveiling the possibility of quantum computing in medical and biomedical zones 249
- 19 Quantum computing-assisted machine learning to improve the prediction of cardiovascular disease in healthcare system 265
- 20 Mitigating the risk of quantum computing in cyber security era 283
- 21 IoMT-based data aggregation using quantum learning 301
- Index 319
