On Depth, Robustness and Performance Using the Data Re-Uploading Single-Qubit Classifier

Easom, Philip, Bouridane, Ahmed, Belatreche, Ammar and Jiang, Richard (2021) On Depth, Robustness and Performance Using the Data Re-Uploading Single-Qubit Classifier. IEEE Access, 9. pp. 65127-65139. ISSN 2169-3536

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Official URL: https://doi.org/10.1109/ACCESS.2021.3075492


Quantum machine learning (QML) is a new field in its infancy, promising performance enhancements over many classical machine learning (ML) algorithms. Data reuploading is a QML algorithm with a focus on utilizing the power of a singular qubit as an individually capable classifier. Recently, there have been studies set out to explore the concept of data re-uploading in a classification setting, however, important aspects are often not considered in experiments, which may hinder our understanding of the methodology’s performance. In this work, we conduct an analysis of the single-qubit data re-uploading methodology, in relation to the effect that system depth has on classification and robustness performances against the influence of environmental noise during training. This is aimed towards bridging together previous works in order to solidify the concepts of the methodology, and provide reasonable insight into how transferable the methodology is when applied to non-synthetic data. To further demonstrate the findings, we also analyse the results of a case study using a subset of MNIST data. From this work, our experimental results support that an increase in system depth can lead to higher classification performances, as well as improved stability during training in noisy environments, with the sharpest performance improvements seemingly occurring between 1–3 uploading layer repetitions. Leading on from our experimental results, we suggest areas for further exploration, to ensure we can maximize classification performance when using the data re-uploading methodology.

Item Type: Article
Additional Information: Funding information: Qatar National Research Fund (NPRP11S–0113–180276) Engineering and Physical Sciences Research Council (EP/P009727/1)
Uncontrolled Keywords: Machine learning, quantum computing, quantum machine learning, data re-uploading
Subjects: G400 Computer Science
G500 Information Systems
G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 25 Aug 2021 08:52
Last Modified: 25 Aug 2021 09:00
URI: http://nrl.northumbria.ac.uk/id/eprint/46994

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