Isbn: 9781041106616 - quantum-inspired neural networks: future perspectives and challenges (8 risultati)

Condizione: New.

Condizione: New. Dr. Moolchand Sharma is an Assistant Professor at the Maharaja Agrasen Institute of Technology, GGSIPU Delhi. He has several publications in reputed international journals and conferences, including SCI-indexed and Scopus-indexed journals. He has .

Buch. Condizione: Neu. Neuware - The rapid development in AI and quantum computing has resulted in a new domain termed Quantum-Inspired Neural Networks (QINNs). These models utilize ideas from quantum mechanics, including superposition, entanglement, and quantum probability, to improve the efficiency and performance of classical neural networks. This book examines the theoretical underpinnings, frameworks, and practical implementations of QINNs, rendering it an essential resource for scholars, academics, and industry experts. It examines mathematical frameworks behind quantum-inspired models, their implementation methodologies, and their relevance in diverse fields, including healthcare, finance, cybersecurity, and natural language processing. It serves as a comprehensive guide for individuals seeking to comprehend and apply QINNs in practical situations, utilizing theoretical insights, algorithmic frameworks, and case examples. The book is distinct due to its emphasis on the present and future of quantum-inspired deep learning. It integrates discussions on hybrid quantum-classical architectures, optimization strategies, and scalability difficulties, addressing the gap between quantum computing and classical AI, which are often treated separately in previous literature. Furthermore, it examines the constraints and future potential of QINNs, providing a framework for the shift from traditional deep learning to quantumaugmented models. Readers will acquire a profound comprehension of how quantum-inspired methodologies might transform the AI domain and propel innovation in nascent technologies.Key Features: - Investigates the integration of quantum computing concepts with neural networks, a dynamically advancing domain with transformational capabilities - Connects quantum computing, artificial intelligence, and machine learning, making it applicable across several fields - Appeals to both academic researchers and industry professionals by addressing theoretical advancements and practical applications - Explores the security implications of quantum AI and ethical concerns, making it relevant for policymakers and tech leaders - Caters to researchers, academics, AI practitioners, and students looking to explore next-gen AI technologies.…

Condizione: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.…

Hardcover. Condizione: Brand New. 316 pages. 6.38x0.92x9.45 inches. In Stock.

Hardcover. Condizione: new. Hardcover. The rapid development in AI and quantum computing has resulted in a new domain termed Quantum-Inspired Neural Networks (QINNs). These models utilize ideas from quantum mechanics, including superposition, entanglement, and quantum probability, to improve the efficiency and performance of classical neural networks. This book examines the theoretical underpinnings, frameworks, and practical implementations of QINNs, rendering it an essential resource for scholars, academics, and industry experts. It examines mathematical frameworks behind quantum-inspired models, their implementation methodologies, and their relevance in diverse fields, including healthcare, finance, cybersecurity, and natural language processing. It serves as a comprehensive guide for individuals seeking to comprehend and apply QINNs in practical situations, utilizing theoretical insights, algorithmic frameworks, and case examples. The book is distinct due to its emphasis on the present and future of quantum-inspired deep learning. It integrates discussions on hybrid quantum-classical architectures, optimization strategies, and scalability difficulties, addressing the gap between quantum computing and classical AI, which are often treated separately in previous literature. Furthermore, it examines the constraints and future potential of QINNs, providing a framework for the shift from traditional deep learning to quantumaugmented models. Readers will acquire a profound comprehension of how quantum-inspired methodologies might transform the AI domain and propel innovation in nascent technologies.Key Features:Investigates the integration of quantum computing concepts with neural networks, a dynamically advancing domain with transformational capabilitiesConnects quantum computing, artificial intelligence, and machine learning, making it applicable across several fieldsAppeals to both academic researchers and industry professionals by addressing theoretical advancements and practical applicationsExplores the security implications of quantum AI and ethical concerns, making it relevant for policymakers and tech leadersCaters to researchers, academics, AI practitioners, and students looking to explore next-gen AI technologies The rapid advancement of artificial intelligence and quantum computing has led to the emergence of Quantum-Inspired Neural Networks (QINNs). This book explores their theoretical foundations, architectures, implementations, applications, limitations, and future potential across diverse domains. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Hardcover. Condizione: new. Hardcover. The rapid development in AI and quantum computing has resulted in a new domain termed Quantum-Inspired Neural Networks (QINNs). These models utilize ideas from quantum mechanics, including superposition, entanglement, and quantum probability, to improve the efficiency and performance of classical neural networks. This book examines the theoretical underpinnings, frameworks, and practical implementations of QINNs, rendering it an essential resource for scholars, academics, and industry experts. It examines mathematical frameworks behind quantum-inspired models, their implementation methodologies, and their relevance in diverse fields, including healthcare, finance, cybersecurity, and natural language processing. It serves as a comprehensive guide for individuals seeking to comprehend and apply QINNs in practical situations, utilizing theoretical insights, algorithmic frameworks, and case examples. The book is distinct due to its emphasis on the present and future of quantum-inspired deep learning. It integrates discussions on hybrid quantum-classical architectures, optimization strategies, and scalability difficulties, addressing the gap between quantum computing and classical AI, which are often treated separately in previous literature. Furthermore, it examines the constraints and future potential of QINNs, providing a framework for the shift from traditional deep learning to quantumaugmented models. Readers will acquire a profound comprehension of how quantum-inspired methodologies might transform the AI domain and propel innovation in nascent technologies.Key Features:Investigates the integration of quantum computing concepts with neural networks, a dynamically advancing domain with transformational capabilitiesConnects quantum computing, artificial intelligence, and machine learning, making it applicable across several fieldsAppeals to both academic researchers and industry professionals by addressing theoretical advancements and practical applicationsExplores the security implications of quantum AI and ethical concerns, making it relevant for policymakers and tech leadersCaters to researchers, academics, AI practitioners, and students looking to explore next-gen AI technologies The rapid advancement of artificial intelligence and quantum computing has led to the emergence of Quantum-Inspired Neural Networks (QINNs). This book explores their theoretical foundations, architectures, implementations, applications, limitations, and future potential across diverse domains. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardcover. Condizione: new. Hardcover. The rapid development in AI and quantum computing has resulted in a new domain termed Quantum-Inspired Neural Networks (QINNs). These models utilize ideas from quantum mechanics, including superposition, entanglement, and quantum probability, to improve the efficiency and performance of classical neural networks. This book examines the theoretical underpinnings, frameworks, and practical implementations of QINNs, rendering it an essential resource for scholars, academics, and industry experts. It examines mathematical frameworks behind quantum-inspired models, their implementation methodologies, and their relevance in diverse fields, including healthcare, finance, cybersecurity, and natural language processing. It serves as a comprehensive guide for individuals seeking to comprehend and apply QINNs in practical situations, utilizing theoretical insights, algorithmic frameworks, and case examples. The book is distinct due to its emphasis on the present and future of quantum-inspired deep learning. It integrates discussions on hybrid quantum-classical architectures, optimization strategies, and scalability difficulties, addressing the gap between quantum computing and classical AI, which are often treated separately in previous literature. Furthermore, it examines the constraints and future potential of QINNs, providing a framework for the shift from traditional deep learning to quantumaugmented models. Readers will acquire a profound comprehension of how quantum-inspired methodologies might transform the AI domain and propel innovation in nascent technologies.Key Features:Investigates the integration of quantum computing concepts with neural networks, a dynamically advancing domain with transformational capabilitiesConnects quantum computing, artificial intelligence, and machine learning, making it applicable across several fieldsAppeals to both academic researchers and industry professionals by addressing theoretical advancements and practical applicationsExplores the security implications of quantum AI and ethical concerns, making it relevant for policymakers and tech leadersCaters to researchers, academics, AI practitioners, and students looking to explore next-gen AI technologies The rapid advancement of artificial intelligence and quantum computing has led to the emergence of Quantum-Inspired Neural Networks (QINNs). This book explores their theoretical foundations, architectures, implementations, applications, limitations, and future potential across diverse domains. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…