Journal Cover: Machine Learning Transfer Efficiencies for Noisy Quantum Walks
Our paper Machine Learning Transfer Efficiencies for Noisy Quantum Walks is now published in Advanced Quantum Technologies journal and is featured on the journal cover.
Quantum walks is a tool for studying various phenomena in quantum systems, including quantum transport in complex networks. In article number 1900115, Alexey A. Melnikov and co‐workers suggest how to improve our understanding of noisy quantum walks in networks with computer vision. The cover gives a pictorial view of a unique eye that learns to observe differences between quantum and classical effects in the space of surrounding networks.