TY - JOUR
T1 - On Depth, Robustness and Performance Using the Data Re-Uploading Single-Qubit Classifier
AU - Easom - McCaldin, Philip
AU - Bouridane, Ahmed
AU - Belatreche, Ammar
AU - Jiang, Richard
N1 - Funding Information:
This work was supported by the NPRP from the Qatar National Research Fund (a member of the Qatar Foundation) under Grant NPRP11S–0113–180276, and in part by the EPSRC under Grant EP/P009727/1.
Publisher Copyright:
© 2013 IEEE.
PY - 2021/5/5
Y1 - 2021/5/5
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 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.
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 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.
KW - Machine learning
KW - data re-uploading
KW - quantum computing
KW - quantum machine learning
UR - http://www.scopus.com/inward/record.url?scp=85105071002&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2021.3075492
DO - 10.1109/ACCESS.2021.3075492
M3 - Article
SN - 2169-3536
VL - 9
SP - 65127
EP - 65139
JO - IEEE Access
JF - IEEE Access
M1 - 9415627
ER -