Abstract

For Quantum Reservoir Computing (QRC), we discuss the relationship between the reservoir’s performance for quantum machine learning (QML) and its physical properties in terms of the optical absorption. We also address expressivity measures in QRC: in establishing a link between gate-based QML and QRC, we show that the role of the reservoir itself is of little impact for its capability to produce non-linear output functions of a given input. For currently suggested input-encoding schemes, no exponential advantage of the quantum system can be exploited, posing the general question of quantum advantage in QML.