Quantum-Inspired Neural Networks by Sharma, Moolchand; Bacanin, Nebojsa; Rashid, Tarik Ahmed;

Short description:

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.

Long description:

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

Table of Contents:

Preface. 1. Fundamentals of Quantum-Inspired Neural Computing. 2. Quantum-Inspired Neural Networks for Cybersecurity: Advanced Threat Detection and Defense Mechanisms. 3. Fundamentals of Quantum Computing and Neural Networks. 4. Quantum-Inspired Computing: Classical Approaches to Machine Learning. 5. Concrete Cryptanalysis of LWE: Advances, Benchmarks, and Experimental Realities. 6. Ethical and societal implications of Quantum inspired AI: Aristotelian moral philosophy perspective. 7. Quantum-Inspired Approaches to Computer Vision: Current State and Future Prospects. 8. Quantum-Inspired Approaches in Healthcare and Bioinformatics. 9. Quantum Neural Networks: Bridging Quantum Computing and Artificial Intelligence. 10. Next-Generation Optimization in Quantum and Hybrid Neural Frameworks. 11. Quantum-Inspired Child-Drawing Optimization for Efficient Graph Neural Network Training. Index.