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Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. Quantum Machine Learning sets the scene for a deeper understanding of the subject for readers of different backgrounds. The author has carefully constructed a clear comparison of classical learning algorithms and their quantum counterparts, thus making differences in computational complexity and learning performance apparent. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications. - Bridges the gap between abstract developments in quantum computing with the applied research on machine learning - Provides the theoretical minimum of machine learning, quantum mechanics, and quantum computing - Gives step-by-step guidance to a broader understanding of this emergent interdisciplinary body of research
As industries are rapidly being digitalized and information is being more heavily stored and transmitted online, the security of information has become a top priority in securing the use of online networks as a safe and effective platform. With the vast and diverse potential of artificial intelligence (AI) applications, it has become easier than ever to identify cyber vulnerabilities, potential threats, and the identification of solutions to these unique problems. The latest tools and technologies for AI applications have untapped potential that conventional systems and human security systems cannot meet, leading AI to be a frontrunner in the fight against malware, cyber-attacks, and various security issues. However, even with the tremendous progress AI has made within the sphere of security, it’s important to understand the impacts, implications, and critical issues and challenges of AI applications along with the many benefits and emerging trends in this essential field of security-based research. Research Anthology on Artificial Intelligence Applications in Security seeks to address the fundamental advancements and technologies being used in AI applications for the security of digital data and information. The included chapters cover a wide range of topics related to AI in security stemming from the development and design of these applications, the latest tools and technologies, as well as the utilization of AI and what challenges and impacts have been discovered along the way. This resource work is a critical exploration of the latest research on security and an overview of how AI has impacted the field and will continue to advance as an essential tool for security, safety, and privacy online. This book is ideally intended for cyber security analysts, computer engineers, IT specialists, practitioners, stakeholders, researchers, academicians, and students interested in AI applications in the realm of security research.
The relentless growth of data in financial markets has boosted the demand for more advanced analytical tools to facilitate and improve financial planning. The ability to constructively use this data is limited for managers and investors without the proper theoretical support. Within this context, there is an unmet demand for combining analytical finance methods with business analytics topics to inform better investment decisions. Advancement in Business Analytics Tools for Higher Financial Performance explores the financial applications of business analytics tools that can help financial managers and investors to better understand financial theory and improve institutional investment practices. This book explores the value extraction process using more accurate financial data via business analytical tools to help investors and portfolio managers develop more modern financial planning processes. Covering topics such as financial markets, investment analysis, and statistical tools, this book is ideal for accountants, data analysts, researchers, students, business professionals, academicians, and more.
Today's supply chains are becoming more complex and interconnected. As a result, traditional optimization engines struggle to cope with the increasing demands for real-time order fulfillment and inventory management. With the expansion and diversification of supply chain networks, these engines require additional support to handle the growing complexity effectively. This poses a significant challenge for supply chain professionals who must find efficient and cost-effective solutions to streamline their operations and promptly meet customer demands. Quantum Computing and Supply Chain Management: A New Era of Optimization offers a transformative solution to these challenges. By harnessing the power of quantum computing, this book explores how supply chain planners can overcome the limitations of traditional optimization engines. Quantum computing's ability to process vast amounts of data from IoT sensors in real time can revolutionize inventory management, resource allocation, and logistics within the supply chain. It provides a theoretical framework and practical examples to illustrate how quantum algorithms can enhance transparency, optimize dynamic inventory allocation, and improve supply chain resilience.
This book presents select proceedings of the 3rd International Conference on “Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects” (Com-IT-Con 2023) held at the Manav Rachna University in July 2023. It covers topics such as artificial intelligence and business analytics, virtual/augmented reality, quantum information systems, cyber security, data science, and machine learning. The book is useful for researchers and professionals interested in the broad field of communication engineering.
This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards. The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years.
This book contains expanded versions of research papers presented at the international sessions of Annual Conference of the Japanese Society for Artificial Intelligence (JSAI), which was held online in June 2020. The JSAI annual conferences are considered key events for our organization, and the international sessions held at these conferences play a key role for the society in its efforts to share Japan’s research on artificial intelligence with other countries. In recent years, AI research has proved of great interest to business people. The event draws both more and more presenters and attendees every year, including people of diverse backgrounds such as law and the social sciences, in additional to artificial intelligence. We are extremely pleased to publish this collection of papers as the research results of our international sessions.
Technology’s rapid advancement has revolutionized how organizations gather, analyze, and utilize data. In this dynamic landscape, integrating artificial intelligence (AI) into business intelligence (BI) systems has emerged as a critical factor for driving informed decision-making and maintaining competitive advantage. This integration allows business to respond quickly to market changes, personalize customer experiences, and optimize operations with greater precision. As AI-driven BI tools continue to evolve, they empower organizations to harness vast amounts of data more effectively, making strategic decisions that are both timely and data-driven, thereby securing their position in an increasingly competitive marketplace. AI-Powered Business Intelligence for Modern Organizations provides a comprehensive overview of this transformative intersection, addressing the diverse challenges, opportunities, and future trends in this field. By exploring the integration of AI into BI systems, the text delves into how advanced analytics, machine learning, and automation are reshaping the way businesses operate. Covering topics such as augmented analytics, decision-making, and sustainability metrics, this book is an excellent resource for business leaders and executives, data scientists and analysts, IT and technology managers, academicians, researchers, graduate and postgraduate students, consultants, industry experts, and more.