Artificial Intelligence and Quantum Computing: Early Innovations. Volume 2
Applications
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Beschreibung
This book extends the first volume with contributions that advance the state of the art and demonstrate their impact in real-world applications. In a few years, the convergence of artificial intelligence (AI) and quantum computing has moved from speculation to concrete prototypes and tools.
Key Highlights
• Health and Precision Medicine: Deep learning and hybrid quantum–classical models support osteoporosis screening from dental radiographs, Alzheimer’s staging with 3D MRI, EEG-based cognitive-state assessment, and HIV/TB biomarker classification, strengthening prevention, diagnosis, and personalized treatment.
• Education, Human–AI Interaction and Fairness: An automatic reviewer of handwritten flowcharts scales feedback in programming courses, while a benchmarking study on speaker ID and gender recognition analyzes bias; quantum granular computing provides a Hilbert-space framework for advanced teaching and research.
• Sustainable Production, Cities, and Environment: Vision systems for welding defects, cucumber disease detection, and analogue water-meter digitization improve manufacturing quality, reduce food waste, and enable transparent water management, alongside work on electric fleets, mobile robots, and sea turtle monitoring.
• Data-Centric and Trustworthy AI for the Digital Economy: GAN-based synthetic tabular data, decision-support for cryptocurrency markets, and benchmarks for intrusion detection and malware classification offer tools for scarce or volatile data, with emphasis on robustness, interpretability, and reproducible evaluation.
• Quantum Algorithms, Hardware and Software Infrastructure: Chapters address quantum optimization (QAOA, adiabatic 3-SAT), quantum error correction and superconducting devices, plus experimental QKD, while a C-to-QIR compiler and quantum granules framework provide software and conceptual support for hybrid systems.
•Optimization and Intelligent Systems: Fuzzy fractal search and fuzzy dragonfly metaheuristics optimize neural architectures and controllers and, together with dynamic hypernetworks and RL-based fleet planning, show how advanced optimization can be embedded in practical engineering workflows.
This book is aimed at researchers, graduate students, and practitioners seeking rigorous, application-oriented work at the AI–quantum interface. By linking methodological advances with SDG-aligned use cases in health, education, infrastructure, energy, and security, it offers a concise blueprint for how quantum-aware intelligent technologies can support more resilient and sustainable societies.
This book extends the first volume with contributions that advance the state of the art and demonstrate their impact in real-world applications. In a few years, the convergence of artificial intelligence (AI) and quantum computing has moved from speculation to concrete prototypes and tools.
Key Highlights
• Health and Precision Medicine: Deep learning and hybrid quantum–classical models support osteoporosis screening from dental radiographs, Alzheimer’s staging with 3D MRI, EEG-based cognitive-state assessment, and HIV/TB biomarker classification, strengthening prevention, diagnosis, and personalized treatment.
• Education, Human–AI Interaction and Fairness: An automatic reviewer of handwritten flowcharts scales feedback in programming courses, while a benchmarking study on speaker ID and gender recognition analyzes bias; quantum granular computing provides a Hilbert-space framework for advanced teaching and research.
• Sustainable Production, Cities, and Environment: Vision systems for welding defects, cucumber disease detection, and analogue water-meter digitization improve manufacturing quality, reduce food waste, and enable transparent water management, alongside work on electric fleets, mobile robots, and sea turtle monitoring.
• Data-Centric and Trustworthy AI for the Digital Economy: GAN-based synthetic tabular data, decision-support for cryptocurrency markets, and benchmarks for intrusion detection and malware classification offer tools for scarce or volatile data, with emphasis on robustness, interpretability, and reproducible evaluation.
• Quantum Algorithms, Hardware and Software Infrastructure: Chapters address quantum optimization (QAOA, adiabatic 3-SAT), quantum error correction and superconducting devices, plus experimental QKD, while a C-to-QIR compiler and quantum granules framework provide software and conceptual support for hybrid systems.
•Optimization and Intelligent Systems: Fuzzy fractal search and fuzzy dragonfly metaheuristics optimize neural architectures and controllers and, together with dynamic hypernetworks and RL-based fleet planning, show how advanced optimization can be embedded in practical engineering workflows.
This book is aimed at researchers, graduate students, and practitioners seeking rigorous, application-oriented work at the AI–quantum interface. By linking methodological advances with SDG-aligned use cases in health, education, infrastructure, energy, and security, it offers a concise blueprint for how quantum-aware intelligent technologies can support more resilient and sustainable societies.
Shows the transformative potential of hybrid classical-quantum algorithms Presents thorough research on quantum evolutionary algorithms, optimization, and deep learning Covers the crucial role of quantum computing in optimization
Autor*in
Oscar Humberto Montiel Ross
Themen in »Artificial Intelligence and Quantum Computing: Early Innovations. Volume 2«
Hybrid Classical-Quantum Algorithms Research Quantum Computing in Medical Imaging Machine Learning and AI Integration Optimization with Quantum Algorithms Deep Learning for Document Classification AI in Agricultural Produce Inspection Infrastructure Monitoring Using AI Big Data Science in Astronomy Quantum Neural Networks for Diagnostics Type-2 and Type-3 Fuzzy Systems: Theory and Applications AI for endangered species identification