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: 4
DOI: https://doi.org/10.59646/809/04
Author: Dr. S. Raju
Abstract
Quantum Intelligence represents an emerging research paradigm that combines quantum computing principles with advanced Artificial Intelligence (AI) to address computationally intensive learning, optimization, simulation, and decision-making problems. Quantum computing exploits quantum phenomena such as superposition, entanglement, and interference to process information through fundamentally different computational mechanisms from classical systems. When integrated with machine learning, optimization algorithms, neural networks, and generative AI, quantum technologies may provide new approaches to feature mapping, parameter optimization, probabilistic modelling, and complex search problems. This chapter examines the conceptual foundations of Quantum Intelligence and investigates the integration of quantum computing with advanced AI architectures. A structured methodology is developed to evaluate quantum-enhanced learning and optimization against classical approaches. A mathematical framework models a hybrid quantum–classical learning system using parameterized quantum circuits, quantum states, expectation values, and optimization functions. Synthesized results indicate potential improvements in optimization efficiency, classification performance, and computational scalability for selected problem classes, while recognizing current limitations involving noise, qubit counts, circuit depth, measurement overhead, and hardware accessibility.
Keywords: Quantum Intelligence, Quantum Computing, Quantum Machine Learning, Hybrid AI, Variational Quantum Circuits, Quantum Optimization