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Variational quantum machine learning algorithms have become the focus ofrecent research on how to utilize near-term quantum devices for machinelearning tasks. They are considered suitable for this as the circuits that arerun can be tailored to the device, and a big part of the computation isdelegated to the classical optimizer. It has also been hypothesized that theymay be more robust to hardware noise than conventional algorithms due to theirhybrid nature. However, the effect of training quantum machine learning modelsunder the influence of hardware-induced noise has not yet been extensivelystudied. In this work, we address this question for a specific type oflearning, namely variational reinforcement learning, by studying itsperformance in the presence of various noise sources: shot noise, coherent andincoherent errors. We analytically and empirically investigate how the presenceof noise during training and evaluation of variational quantum reinforcementlearning algorithms affect the performance of the agents and robustness of thelearned policies. Furthermore, we provide a method to reduce the number ofmeasurements required to train Q-learning agents, using the inherent structureof the algorithm.

Robustness of quantum reinforcement learning under hardware errors