Abstract

Quantum Machine Learning (QML), leveraging principles of superposition, entanglement, and quantum parallelism, provides a promising pathway toward overcoming these limitations. This paper investigates advanced hybrid quantum–classical architectures designed for efficient high-dimensional data processing. Through detailed exploration of variational quantum circuits, quantum embedding techniques, and quantum kernel methods, we analyze the strengths and limitations of quantum models on Noisy Intermediate-Scale Quantum (NISQ) hardware. Comparative assessments against classical baselines demonstrate that carefully structured hybrid models can achieve improved expressive power and computational efficiency, particularly on complex, non-linear datasets. The study presents architectural guidelines, performance analyses, and insights into quantum-specific advantages for future scalable QML systems.