In ensuring an inclusive digital environment, web accessibility plays an
important role. The existing AI-based techniques face challenges in handling the
dynamism of modern web structures. This chapter presents a comparative analysis of
Quantum AI and classical AI techniques used for web accessibility evaluation. A
comparative analysis approach is applied to examine the key accessibility parameters
using a real-world dataset, efficiency, accuracy, and computational overheads of both
approaches. The findings indicate that in the structured evaluation tasks, the classical
AI performed well. By leveraging the computational advantages of QAI, processing
time can be reduced, and the precision can be enhanced. QAI demonstrates superior
efficiency in processing complex and large-scale accessibility assessments.
The present research provides important perspectives on the evolving role of QAI in
web accessibility evaluation, offering a foundation for future advancements in
automated assessment techniques.
Keywords: Accessibility testing, Artificial Intelligence (AI), Automated evaluation, Decoherence, Entanglement, Fidelity, Human-Computer Interaction (HCI), Interference, Machine learning models, Quantum Machine Learning (QML), Quantum optimization, Quantum-enhanced accessibility, Quantum AI, Quantum computing, Qubit, Quantum circuit, Sensor integration, Superposition, User Experience (UX), Web accessibility standards.
