Quantum Computing

GANs represent a generative model leveraging the growing power of deep learning (DL) algorithms. This book presents the breakthroughs, applications and latest research innovations of GANs and of quantum GANs. In particular, the book includes contributions on Autoencoders and Variational Autoencoders, Semi-Supervised GANs, Quantum GANs and Quantum Data Privacy, and Quantum GANs for image manipulation.

Quantum computing has shown a potential to tackle specific types of problems, especially those involving a daunting number of variables, at an exponentially faster rate compared to classical computers. This volume focuses on quantum variants of machine learning algorithms, such as quantum neural networks, quantum reinforcement learning, quantum principal component analysis, quantum support vectors, quantum Boltzmann machines, and many more.

Quantum computing systems are powerful for allowing a client to perform any quantum computations from a remote quantum server while concealing the structure and content of the computation fall under the category of blind quantum computing (BQC). In BQC, the client delegates the quantum processing to one or more powerful quantum servers while retaining privacy over the input, computation and output. This makes it suitable for secure quantum cloud computing. This feature is powerful to ensure that even untrusted servers cannot learn the details of the user's computation. With quantum computing, there is a fast-growing need to transition from general-purpose quantum systems to customized architectures tailored to specific application requirements. This transition is critical while considering sustainability goals and financial limitations. With this advanced computing architecture, a custom system can optimize energy use, hardware complexity, and resource allocation to better serve individual user needs while staying within budgetary boundaries.

Quantum computers have demonstrated that they have the inherent potential to outperform classical computers in many areas. One of the major impacts is that the currently available cryptography algorithms are bound to no longer hold once quantum computers are able to compute at full speed. This book presents an overview of all the cross-disciplinary developments in cybersecurity that are being generated by the advancements in quantum computing.

This book is to explore and explain the strategically sound capabilities at the synchronization between quantum computing and artificial intelligence (AI). The reader will be presented with an introduction and a deeper review of the technological trends and transitions being unearthed in the quantum computing and AI domains.

The book explores how quantum computing can protect AI systems from sophisticated adversarial threats. As AI applications grow in sectors like finance, healthcare, and critical infrastructure, vulnerabilities such as data poisoning and model inversion pose significant security risks. This book investigates how quantum methods can effectively counter these challenges.
Distinct from existing literature, it combines theoretical insights with practical applications, covering quantum-enhanced cryptography, quantum machine learning for threat detection, and advanced algorithms tailored for AI. It provides actionable solutions for AI practitioners while also meeting the academic needs of researchers with rigorous mathematical treatments.
The book is targeted at academic researchers, professionals, and graduate students in computer science interested in AI and quantum computing. It goes beyond mathematical concepts to bridge quantum theory and its practical applications across sciences and industries. By offering both foundational principles and advanced quantum defense applications, this book serves as a vital resource for those developing AI systems with robust, quantum-enhanced security.