Robustness of quantum reinforcement learning under hardware errors
- URL: http://arxiv.org/abs/2212.09431v1
- Date: Mon, 19 Dec 2022 13:14:22 GMT
- Title: Robustness of quantum reinforcement learning under hardware errors
- Authors: Andrea Skolik, Stefano Mangini, Thomas B\"ack, Chiara Macchiavello,
Vedran Dunjko
- Abstract summary: Variational quantum machine learning algorithms have become the focus of recent research on how to utilize near-term quantum devices for machine learning tasks.
They are considered suitable for this as the circuits that are run can be tailored to the device, and a big part of the computation is delegated to the classical.
However, the effect of training quantum machine learning models under the influence of hardware-induced noise has not yet been extensively studied.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Variational quantum machine learning algorithms have become the focus of
recent research on how to utilize near-term quantum devices for machine
learning tasks. They are considered suitable for this as the circuits that are
run can be tailored to the device, and a big part of the computation is
delegated to the classical optimizer. It has also been hypothesized that they
may be more robust to hardware noise than conventional algorithms due to their
hybrid nature. However, the effect of training quantum machine learning models
under the influence of hardware-induced noise has not yet been extensively
studied. In this work, we address this question for a specific type of
learning, namely variational reinforcement learning, by studying its
performance in the presence of various noise sources: shot noise, coherent and
incoherent errors. We analytically and empirically investigate how the presence
of noise during training and evaluation of variational quantum reinforcement
learning algorithms affect the performance of the agents and robustness of the
learned policies. Furthermore, we provide a method to reduce the number of
measurements required to train Q-learning agents, using the inherent structure
of the algorithm.
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