Masters Research on Quantum ML/DL/ViT

This research falls under the rapidly expanding field of Hybrid Quantum-Classical Machine Learning (HQML). It explores how to merge the established processing power of classical ML/DL with the theoretical advantages of Quantum Computing to solve complex classification problems. Rather than attempting to build a purely quantum model which is currently impossible for massive datasets due to hardware constraints this approach operates like a computational relay race. Classical algorithms handle the heavy lifting of data compression, passing a refined, low-dimensional "latent vector" to a quantum circuit. The quantum circuit then maps this data into a highly complex, multidimensional quantum state to identify subtle patterns that classical algorithms might miss.