000			06203nam a22003137a 4500
003			OSt
005			20260807095744.0
008			260807b |||||||| |||| 00| 0 eng d
020			_a9789362241931 [hardbound]
040			_aUniversity of Cebu-Banilad _cUniversity of Cebu-Banilad
245			_aArtificial intelligence and quantum computing / _cedited by Jens Schweinfurth.
260			_aNew Delhi, India : _bDiscovery Publishing House, _cc2025.
300			_a269 pages : _billustration (black and white) ; _c24 cm.
336			_2rdacontent _atext
337			_2rdamedia _aunmdiated
338			_2rdacarrier _avolume
504			_aIncludes bibliographical references and index.
505			_aContents: Chapter 1 Introduction to artificial intelligence -- Chapter 2 Search algorithms in AI -- Chapter 3 Expert systems in AI -- Chapter 4 Knowledge representation in AI -- Chapter 5 Artificial intelligence in manufacturing -- Chapter 6 Artificial intelligence and global risk -- Chapter 7 Quantum computing -- Chapter 8 Quantum cryptography.
520			_a"In the age of rapid technological evolution, the fusion of quantum computing and artificial intelligence (AI) emerges as a groundbreaking intersection that promises unparalleled computational prowess and advanced intelligence. Quantum computing (QC) is gaining popularity at an accelerating pace, its adaptability attracting significant attention and fostering its growth. Leading enterprises worldwide, research institutions, startups, and organizations with sufficient resources are all contributing to the advancement of this remarkable sector. While there are skeptics who doubt QC's ability to achieve the exceptional results it claims, many are enthusiastic about the potential solutions it offers for modern-day challenges that lie beyond the reach of traditional classical computing (CC). It is essential to understand that QC is not a technique or paradigm designed to replace or suppress CC. Instead, it aims to complement and enhance classical computing by addressing areas where classical methods may lag. As quantum devices become increasingly complex, groundbreaking experimental work is demonstrating the potential of machine learning approaches to develop and automate new quantum technologies. Among the primary challenges in scaling up contemporary quantum computing platforms are reliable fabrication, designing large arrays, and the time-consuming procedures necessary to achieve the high-level control required to operate quantum devices. To address these challenges, a new field is emerging at the intersection of quantum devices and artificial intelligence. This field leverages the versatility and generalization ability of Al to achieve optimal quantum control. The convergence of QC and Al holds the promise of transforming various industries by solving complex problems more efficiently than ever before. As we continue to explore and develop this fusion, we can expect to see significant advancements in both the theoretical and practical aspects of quantum machine learning. This evolution is poised to revolutionize the way we approach computational problem-solving, offering innovative solutions and pushing the boundaries of what is possible with current technology. This book targets this emerging field, focusing on advances in machine-learning-enhanced control, calibration, and fabrication of quantum devices in a range of quantum computing platforms. Of special interest is the application of machine learning methods to experiments, focusing on the control of quantum circuits as well as machine learning software for quantum devices. The book covers the theoretical foundations and practical applications of integrating artificial intelligence (AI) with quantum computing (QC), offering a thorough understanding of the principles, methodologies, and advancements in this burgeoning field. By presenting cutting-edge research, innovative algorithms, and real-world case studies, the book provides valuable insights into how quantum computing can enhance Al capabilities and vice versa. Topics covered include quantum learning theory, quantum deep learning, quantum convolutional neural networks, quantum transfer learning, and quantum optimization algorithms, among others. The book will be of immense useful to Al researchers looking to leverage quantum computing for developing more powerful algorithms, quantum computing experts seeking to apply their knowledge to Al challenges, and students of computer science, physics, and engineering who are interested in gaining a holistic view of these interdisciplinary domains. Additionally, professionals in technology sectors, including software developers, data scientists, and IT managers, will find this book useful for understanding the potential applications and implications of integrating Al and QC in various industries. By addressing both foundational concepts and advanced topics, the book aims to bridge the knowledge gap between Al and QC communities, fostering collaboration and innovation. Finally, this book aspires to be an essential resource that equips its readers with the knowledge and tools needed to navigate and contribute to the future of these revolutionary technologies. I would like to express my sincere gratitude to the numerous individuals and organizations whose contributions have enriched the content of this book. This work would not have been possible without the valuable information sourced from widely regarded references. I extend my heartfelt thanks to the editors, researchers, and experts whose pioneering work has laid the foundation for the topics covered in this book. Their dedication to advancing knowledge in their respective fields has been instrumental in shaping the content presented here." --Preface
521			_aAdult
541			_aPurchased _xOrtega, Eric _yCollege of Computer Studies _zComputer Science
546			_aText in English
650			_aArtificial intelligence.
650			_aQuantum computing.
700			_aSchweinfurth, Jens, _eeditor.
942			_2ddc _cBK
998			_cJanna [new] _d08/07/2026
999			_c15578 _d15578