ИСТИНА

This work presents a comparative analysis of the Quantum Autoencoder (QAE), a hybrid quantum classical model adapted from classical deep learning. As quantum machine learning gains prominence as a near-term application of quantum computing across a wide range of tasks, we investigate the potential of QAE to deliver practical advantages. The study aims to benchmark the performance of a quantum autoencoder against its classical counterpart, exploring hypothesized benefits such as parameter efficiency, superior data compression, and improved convergence. Our results contribute to understanding both the practical viability and the current limitations of quantum autoencoder models on near-term hardware.