Publication Type
Journal Article
Version
publishedVersion
Publication Date
7-2022
Abstract
One of the most promising areas of research to obtain practical advantage is Quantum Machine Learning which was born as a result of cross-fertilisation of ideas between Quantum Computing and Classical Machine Learning. In this paper, we apply Quantum Machine Learning (QML) frameworks to improve binary classification models for noisy datasets which are prevalent in financial datasets. The metric we use for assessing the performance of our quantum classifiers is the area under the receiver operating characteristic curve AUC-ROC. By combining such approaches as hybrid-neural networks, parametric circuits, and data re-uploading we create QML inspired architectures and utilise them for the classification of non-convex 2 and 3-dimensional figures. An extensive benchmarking of our new FULL HYBRID classifiers against existing quantum and classical classifier models, reveals that our novel models exhibit better learning characteristics to asymmetrical Gaussian noise in the dataset compared to known quantum classifiers and performs equally well for existing classical classifiers, with a slight improvement over classical results in the region of the high noise.
Discipline
Artificial Intelligence and Robotics | Theory and Algorithms
Research Areas
Intelligent Systems and Optimization
Publication
Scientific Reports
Volume
12
Issue
1
First Page
1
Last Page
12
ISSN
2045-2322
Identifier
10.1038/s41598-022-14876-6
Publisher
Nature Research
Citation
Schetakis, N.; Aghamalyan, D.; GRIFFIN, Paul Robert; and Boguslavsky, M..
Review of some existing QML frameworks and novel hybrid classical-quantum neural networks realising binary classification for the noisy datasets. (2022). Scientific Reports. 12, (1), 1-12.
Available at: https://ink.library.smu.edu.sg/sis_research/7214
Copyright Owner and License
Authors-CC-BY
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.
Additional URL
https://doi.org/10.1038/s41598-022-14876-6