Quantum data encoding in QML: Taxonomy and selection framework

This project presents a systematic review of quantum data encoding methods in quantum machine learning, focusing on their role as a critical bottleneck in NISQ-era systems. By analyzing recent literature, the study develops a unified taxonomy of encoding techniques and examines trade-offs related to expressivity, resource usage, noise robustness, and trainability. Based on this synthesis, the project proposes a hardware-aware decision framework, Adaptive Hybrid Quantum Encoding (AHQE), which formulates encoding selection as a resource-constrained optimization problem. The goal is to provide both a structured understanding of existing methods and a principled approach to selecting encoding strategies under real-world quantum hardware constraints.