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: 7
DOI: https://doi.org/10.59646/809/07
Author: N Pushpa
Abstract
AI–Quantum–Bio convergence represents an emerging interdisciplinary paradigm in which Artificial Intelligence, quantum computing, and biological sciences interact to create intelligent computational and biological ecosystems. Artificial Intelligence contributes learning, prediction, automation, and decision-making; quantum technologies provide novel computational capabilities for optimization, simulation, and information processing; and biological systems contribute complex molecular structures, adaptive mechanisms, biosensing, and bio-inspired intelligence. This chapter examines the theoretical foundations and technological interactions among these three domains and proposes an integrated framework for future intelligent ecosystems. A hybrid methodology is developed to evaluate AI–quantum–bio systems across prediction accuracy, optimization quality, biological modelling, adaptability, computational efficiency, and system reliability. An adaptive algorithm is proposed for selecting appropriate classical, quantum, and biological computational resources according to task requirements. A mathematical model integrates AI learning, quantum processing, biological complexity, feedback, uncertainty, and system-level optimization. Synthesized results indicate substantial potential for integrated applications in drug discovery, precision biotechnology, synthetic biology, healthcare, materials discovery, and environmental monitoring, while highlighting challenges involving scalability, data integration, quantum noise, biological variability, ethics, security, and governance.
Keywords: AI–Quantum–Bio Convergence, Artificial Intelligence, Quantum Computing, Biotechnology, Intelligent Ecosystems, Computational Biology