This book explores the exciting field of quantum computing, which is changing how we approach computation. It covers the basics, cybersecurity aspects, advanced machine learning techniques, and the many ways quantum computing can be used. Quantum computing is much more powerful than traditional computing. The book starts by explaining the core concepts like qubits, quantum gates, superposition, entanglement, quantum memory, and quantum parallelism. One important area is how quantum computing can improve machine learning for cybersecurity. It can handle huge amounts of data and find complex patterns faster than regular computers. This is especially useful for finding cyber threats in real time, such as spotting unusual activity in healthcare networks that might mean a security breach. Quantum machine learning can help healthcare organizations better defend against advanced cyberattacks that try to steal patient data. The book also looks at how quantum computing is changing cybersecurity itself.
It discusses quantum cryptography, post-quantum cryptography, and secure communication, explaining how quantum computing is leading to new ways of encrypting data, detecting threats, and protecting information. Beyond cybersecurity, the book shows how quantum computing impacts many other fields, such as medicine, finance, materials science, and logistics. It is poised to revolutionize artificial intelligence (AI) in healthcare and many other sectors. Because quantum computing is constantly developing, with discoveries and new applications happening all the time, this book brings together researchers from universities and industries to share their latest findings. It aims to help shape the future of this technology. The book offers a solid foundation, detailed explanations of advanced techniques, and a fascinating look at how quantum computing is being used in the real world. As quantum computing becomes easier to access through new tools and cloud platforms, this book hopes to inspire new research in AI and spark innovative applications that were previously thought impossible.
Prateek Singhal is an Assistant Professor in the Department of Computer Science and Engineering at Christ (Deemed University), Delhi-NCR. He is pursuing a Ph.D. in Medical Imaging at the Maharishi University of Information Technology in Lucknow, India. He has almost five years of experience doing research and teaching. He has published multiple research articles in SCI/SCIE/Scopus publications and has spoken at prestigious conferences. His research and scholarly activities centre on the interface of machine learning, artificial intelligence, and healthcare applications. His works include several forms, including journal articles, book chapters, and novels, displaying a dedication to sharing research findings with both academic and general audiences. Singhal's work on using AI and machine learning approaches to analyse medical images has been highlighted in several publications. He holds several national and international patents, some of which are awarded. He has contributed to IEEE and Elsevier, among other publications. He serves on the scientific advisory committee at his current institute. His current interests include image processing, medical imaging, human-computer interfaces, neurocomputing, and the Internet of Things.
Pramod Kumar Mishra is working as a Head and Professor in the Department of Computer Science & Engineering at Banaras Hindu University, Varanasi. He has completed a Ph.D. degree in A study of an efficient shortest path algorithms for serial and parallel computers from APS University, Rewa, India. He has more than thirty years of experience in research and teaching. He has received various Awards and fellowships from well-reputed organisations. He has also received various grants from national and international government bodies/agencies. He has published several research articles in SCI/SCIE/Scopus journals and at conferences of high repute. He has also authored a book on Cloud Computing. He has various National and international patents, and some are granted. He has made contributions to IEEE, Elsevier, etc.. He is on the research advisory team in his present institute. His current areas of interest include AI and Machine Learning Algorithms, Data Analytics, Parallel Computing, High-Performance Clusters, Algorithm Engineering (AE), High-Performance AE, Parallel Computation, and Computational Complexity.
Mokhtar Mohammed Hasan is a Lecturer in the Computer Science Department at the College of Science for Women, University of Baghdad, a position he has held since 2003. He earned his B.Sc. and M.Sc. degrees in Computer Science from Baghdad University, achieving top rankings in both programs. He furthered his education by obtaining a Ph.D. in Computer Science from Banaras Hindu University, India, also graduating first in his class. His work spans a range of topics within computer science, including password security using neural networks, automatic block selection for texture image synthesis using genetic algorithms, and applying Quran security and Hamming codes for text modification prevention. He has also extensively researched gesture recognition systems, exploring various techniques like HSV brightness factor matching, scaled normalisation, and geometric feature analysis. His work in this area has been published in international journals such as the International Journal of Image Processing and the International Journal of Computer Science & Information Technology. More recently, his research has extended to areas like deep learning for disease distinction and blockchain-based student information management systems, with publications in AIP Conference Proceedings and Lecture Notes on Data Engineering and Communications Technologies. His publications demonstrate a sustained contribution to the field of computer science, with a particular focus on image processing, pattern recognition, and security.
