Review
Open access
2026
Quantum Machine Learning: Foundational Principles to Practical Applications
This survey provides a comprehensive overview of the theoretical foundations, unified taxonomy, and key methodologies in QML, and discusses quantum data encoding techniques, variational quantum models, neural-inspired quantum architectures, and quantum data learning approaches, along with their associated challenges and limitations.
Ashis Kumar Pati, Rajesh Vayyala, K. Mohanty et al.
· IEEE Access · 0 citations