Title: AI, Innovation and Global Transformation: Interdisciplinary Perspectives on Technology, Business and Society
Editors: Dr. J. Preetha, and Dr. Siddhartha Mehrotra
ISBN: 978-81-69857-64-2
Chapter: 5
DOI: https://doi.org/10.59646/809/05
Authors: Dr. G. Aarthi, and P. Maria Sheeba
Abstract
Quantum Intelligence represents an emerging interdisciplinary paradigm that integrates quantum algorithms, quantum machine learning, artificial intelligence, and intelligent autonomous systems. Unlike conventional computing, quantum computing exploits superposition, entanglement, and interference to process information using quantum states, creating new computational approaches for optimization, simulation, search, and machine learning. This chapter examines the progression from fundamental quantum algorithms toward intelligent machines capable of learning, reasoning, optimizing, and adapting within complex computational environments. The study investigates quantum machine learning, variational quantum circuits, quantum optimization, quantum neural networks, and hybrid quantum–classical architectures. A structured methodology is employed to compare classical, quantum, and hybrid intelligent systems using accuracy, convergence, optimization quality, adaptability, computational efficiency, and robustness. A mathematical framework is developed to represent quantum-enhanced learning through parameterized quantum states, measurement operators, loss functions, and classical optimization. Synthesized results indicate that hybrid architectures provide promising performance for selected optimization and learning tasks while current limitations remain in quantum noise, scalability, data encoding, circuit depth, and hardware reliability.
Keywords: Quantum Intelligence, Quantum Algorithms, Quantum Machine Learning, Intelligent Machines, Hybrid Quantum Computing, Variational Quantum Circuits