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

Research output: Contribution to journalJournal articlepeer-review

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On Depth, Robustness and Performance Using the Data Re-Uploading Single-Qubit Classifier. / Easom, Philip; Bouridane, Ahmed; Belatreche, Ammar; Jiang, Richard.

In: IEEE Access, 14.04.2021.

Research output: Contribution to journalJournal articlepeer-review

Harvard

APA

Easom, P., Bouridane, A., Belatreche, A., & Jiang, R. (Accepted/In press). On Depth, Robustness and Performance Using the Data Re-Uploading Single-Qubit Classifier. IEEE Access.

Vancouver

Author

Easom, Philip ; Bouridane, Ahmed ; Belatreche, Ammar ; Jiang, Richard. / On Depth, Robustness and Performance Using the Data Re-Uploading Single-Qubit Classifier. In: IEEE Access. 2021.

Bibtex

@article{44bc39c317e84db6b4ed9414f0d5b40e,
title = "On Depth, Robustness and Performance Using the Data Re-Uploading Single-Qubit Classifier",
abstract = "Quantum machine learning (QML) is a new field in its{\textquoteright} 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{\textquoteright}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 performance, and robustness against the influence of environmental noise during training. We do this in an effort to bridge together previous works, 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 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 performance, 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 also suggest areas that for further exploration, to ensure we can maximize classification performance when using the data re-uploading methodology.",
author = "Philip Easom and Ahmed Bouridane and Ammar Belatreche and Richard Jiang",
note = "{\textcopyright}2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2021",
month = apr,
day = "14",
language = "English",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS

TY - JOUR

T1 - On Depth, Robustness and Performance Using the Data Re-Uploading Single-Qubit Classifier

AU - Easom, Philip

AU - Bouridane, Ahmed

AU - Belatreche, Ammar

AU - Jiang, Richard

N1 - ©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2021/4/14

Y1 - 2021/4/14

N2 - 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 performance, and robustness against the influence of environmental noise during training. We do this in an effort to bridge together previous works, 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 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 performance, 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 also suggest areas that for further exploration, to ensure we can maximize classification performance when using the data re-uploading methodology.

AB - 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 performance, and robustness against the influence of environmental noise during training. We do this in an effort to bridge together previous works, 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 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 performance, 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 also suggest areas that for further exploration, to ensure we can maximize classification performance when using the data re-uploading methodology.

M3 - Journal article

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -