Title: Artificial Intelligence Across Disciplines: Research, Innovation, and Intelligent Solutions
Editors: Dr. Subita Bhagat, Dr. A. Balamurugan, Dr. P. Krishna Kumar, and Mrs. S. Nandhini Devi
ISBN: 978-81-69857-83-3
Chapter: 17
DOI: https://doi.org/10.59646/815/17
Author: S. Benitta Sherine
Abstract
The convergence of quantum mechanics and machine intelligence introduces a paradigm shift in computational complexity, high-dimensional optimization, and representational learning. This chapter explores the theoretical principles, architectural designs, and real-world implementations of Quantum Artificial Intelligence (QAI), focusing on noisy intermediate-scale quantum (NISQ) architectures and hybrid quantum-classical frameworks. We investigate the application of parameterized quantum circuits (PQCs) and quantum neural networks (QNNs) within a high-dimensional molecular property prediction and material-discovery case study. By integrating variational quantum eigensolvers, quantum kernel estimation, and Barren-plateau-mitigating gradient methods, the proposed framework captures quantum correlations and spatial-electronic entanglements inaccessible to classical deep representations. Evaluated across physical transmon superconducting quantum processors and fault-tolerant emulators, the hybrid QAI model achieves accelerated parameter convergence, higher fidelity energy surface reconstructions, and substantial compute scaling advantages over classical baseline architectures. This work underscores the potential of quantum machine intelligence to transform complex chemical informatics, portfolio optimization, and multi-scale physical modeling.
Keywords: Quantum Artificial Intelligence, Variational Quantum Circuits, Quantum Neural Networks, Quantum Kernels, NISQ Devices, Molecular Property Prediction.