Aleksandr V. Korolev

Variational Quantum Machine Learning for Binary Classification on NISQ Hardware: Expressibility, Trainability, and Noise Robustness

This study investigates classification performance and trainability of variational quantum classifiers on near-term noisy quantum hardware across circuit depth and qubit count regimes within the context of quantum computing and machine learning, an area of growing scientific importance given its implications for hybrid...