Resilience and Risk in the Postpandemic Era
Engineering Thermodynamics and Recovery Management, wiley, ss.523-537, 2026
- Yayın Türü: Kitapta Bölüm / Araştırma Kitabı
- Basım Tarihi: 2026
- Doi Numarası: 10.1002/9781394389582.ch28
- Yayınevi: wiley
- Sayfa Sayıları: ss.523-537
- Anahtar Kelimeler: entanglement, hybrid computing, machine learning, quantum algorithms, Quantum computing, quantum machine learning, qubits, superposition
- Karadeniz Teknik Üniversitesi Adresli: Evet
Özet
The chapter gives a detailed account of the knowledge and integration journey of both quantum computing and machine learning by ending it with quantum machine learning (QML) applications. It starts with some fundamental aspects of quantum computing and using qubits, superposition, entanglement, and quantum gates as the building blocks of quantum information processing. The next segment is dedicated to significant quantum algorithms, their structures, their computational benefits, and the character of problems that are to be solved. The difference between classical and quantum computing reveals some significant dissimilarities in modeling, encoding information, and utilization of information in solving problems/tasks. Machine learning basics are introduced to create a point of convergence during integration including the types of models, training, and performance analytics. Areas of overlap between the quantum and machine learning have been discussed, and the strengths of both computing paradigms have been highlighted through the path that will be taken by hybrid quantum-classical architecture. Quantum kernels and quantum-enhanced neural networks are some examples of simple algorithms and methods in QML and are classifiable based on their design guidelines, efficiency, and potential to become quantum-advantaged. Practical cumbersome factors entail data-encoding approach, hardware limitation, and worry mitigation with disorderly scaling quantum (NISQ [noisy intermediate-scale quantum]) equipment. Other practical uses of an advantage of QML are considered in the chapter as well, namely, quantum chemistry, economy and finance, healthcare, logistic systems, and optimization. The issues of scalability, noise resistance, and the creation of standardized benchmarks are mentioned along with the trends in the emerging research. With this step-by-step reasoning, the chapter has attempted to provide the readers with clear understanding of theoretical groundwork and the real-life application of QML solutions to different areas.