Published Online:September 2025
Product Name:The IUP Journal of Information Technology
Product Type:Article
Product Code:IJIT010925
DOI:10.71329/IUPJIT/2025.21.3.7-25
Author Name:Ananta Charan Ojha
Availability:YES
Subject/Domain:Engineering
Download Format:PDF
Pages:7-25
The intersection of artificial intelligence (AI) and quantum computing has given rise to the emergence of quantum machine learning (QML) and quantum generative AI (QGAI). These two subfields have the potential to generate synthetic data with high fidelity, analyze high dimensional datasets, and solve complex problems, but face challenges in hardware, algorithms, and ethical aspects. Current literature focuses on these two fields in isolation. It does not explore their combined potential for data synthesis and analysis. This paper conducts a critical review of literature to provide a unified perspective on these fields, synthesizing insights from recent technological advances, experimental applications, and ethical debates. It distinguishes between demonstrated use cases and speculative claims made in support of these fields. It also identifies key challenges and advocates for a forward-thinking research agenda focusing on hardware-software co-design, error mitigation, and quantum-native model development, together with proactive ethical and policy frameworks. It emphasizes the need for a responsible discourse on QML and QGAI for achieving scientific progress and societal benefits, while providing a critical perspective on their current progress and future prospects.
Quantum computers process information using quantum phenomena such as superposition, entanglement, and interference. While a classical computer uses bits (0 and 1) to represent only one state at a time, a quantum computer uses qubits to represent multiple states simultaneously. This ability of a quantum computer enables it to perform massive computation and solve complex problems much faster than classical computers.