Robustness of quantum reinforcement learning under hardware errors

Year: 2023

Authors: Skolik A., Mangini S., Baeck T., Macchiavello C., Dunjko V.

Autors Affiliation: Leiden Univ, Niels Bohrweg 1, NL-2333 CA Leiden, Netherlands; Volkswagen AG, Ungererstr 69, D-80805 Munich, Germany; Univ Pavia, Dipartimento Fis, Via Bassi 6, I-27100 Pavia, Italy; INFN Sez Pavia, Via Bassi 6, I-27100 Pavia, Italy; CNR INO, Largo E Fermi 6, I-50125 Florence, Italy.

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.

Journal/Review: EPJ QUANTUM TECHNOLOGY

Volume: 10 (1)      Pages from: 8-1  to: 8-43

More Information: AcknowledgementsAS is funded by the German Ministry for Education and Research (BMB+F) in the project QAI2-Q-KIS under grant 13N15587. This work was also supported by the Dutch Research Council (NWO/OCW), as part of the Quantum Software Consortium programme (project number 024.003.037). CM acknowledges support by the National Research Centre for HPC, Big Data and Quantum Computing (ICSC: MUR project CN00000013).
KeyWords: Variational quantum algorithms; Quantum machine learning; Quantum hardware noise
DOI: 10.1140/epjqt/s40507-023-00166-1

Citations: 7
data from “WEB OF SCIENCE” (of Thomson Reuters) are update at: 2024-11-17
References taken from IsiWeb of Knowledge: (subscribers only)
Connecting to view paper tab on IsiWeb: Click here
Connecting to view citations from IsiWeb: Click here