Decohering tensor network quantum machine learning models

Abstract Tensor network quantum machine learning (QML) models are promising applications on near-term hardware. While decoherence of qubits is expected to decrease the performance QML models, it unclear what extent diminished can be compensated for by adding ancillas and accordingly increasing virtual bond dimension models. We investigate here competition between classification two with an analysis effect from perspective regression. present numerical evidence that fully decohered unitary tree tensor (TTN) performs at least as well non-decohered TTN, suggesting beneficial add TTN regardless amount may consequently introduced.