Many-Sorted Algebras for Deep Learning and Quantum Technology

Many-Sorted Algebras for Deep Learning and Quantum Technology provides a comprehensive and rigorous introduction to quantum technologies and their relationship to deep learning and quantum theory, with hundreds of examples and visual displays. It explains the algebraic underpinnings of these disciplines and offers closure conditions for specifying an algebraic structure.

Format: Paperback / softback
Length: 350 pages
Publication date: 26 January 2024
Publisher: Elsevier Science & Technology

Many-Sorted Algebras for Deep Learning and Quantum Technology is a comprehensive and detailed exploration of fundamental concepts in quantum technologies and their connections to deep learning and quantum theory. The current convergence of quantum theory and deep learning methodologies highlights the importance of a resource that provides readers with insights into the algebraic foundations of these fields. While various analytical, topological, probabilistic, and geometrical concepts are employed in these areas, algebra serves as the primary thread that unites them. This thread is unveiled through the use of many-sorted algebras, which offer a structured framework for understanding these disciplines.

The book encompasses hundreds of well-designed examples that elucidate the intriguing aspects of quantum systems. These examples are accompanied by numerous visual representations, including the polyadic graph, which illustrates the types or sorts of objects used in quantum or deep learning. It also showcases the inter and intra-sort operations necessary for describing algebras, providing a clear understanding of the closure conditions. Throughout the book, all the laws or equational identities required to specify an algebraic structure are meticulously described, ensuring precise and comprehensive coverage.

By delving into the realm of many-sorted algebras, this text serves as a valuable resource for researchers, students, and practitioners in the fields of quantum technologies, deep learning, and mathematics. It offers a comprehensive understanding of the algebraic foundations that underpin these disciplines, facilitating a deeper appreciation of the complex phenomena and applications that arise in these areas.

Weight: 450g
Dimension: 234 x 191 (mm)
ISBN-13: 9780443136979