Publication Type
Journal Article
Version
acceptedVersion
Publication Date
11-2021
Abstract
This technology offer is a quantum machine learning algorithm applied to binary classification models for noisy datasets which are prevalent in financial and other datasets. By combining hybrid-neural networks, quantum parametric circuits, and data re-uploading we have improved the classification of non-convex 2-dimensional figures by understanding learning stability as noise increases in the dataset. The metric we use for assessing the performance of our quantum classifiers is the area under the receiver operator curve (ROC AUC). We are interested to collaborate with partners with use cases for binary classification of noisy data. Also, as quantum technology is still insufficient for large datasets, we would be interested to work with technology partners for assessing implementation paths.
Keywords
Quantum, Binary Classifiers, Machine learning, AI
Discipline
Databases and Information Systems | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Scientific Reports
First Page
1
Last Page
14
ISSN
2045-2322
Identifier
10.21203/rs.3.rs-1440760/v1
Publisher
Nature Research
Citation
SCHETAKIS, Nikolaos; AGHAMALYAN, Davit; GRIFFIN, Paul Robert; and BOGUSLAVSKY, Michael.
Binary classifiers for noisy datasets: A comparative study of existing quantum machine learning frameworks and some new approaches. (2021). Scientific Reports. 1-14.
Available at: https://ink.library.smu.edu.sg/sis_research/7738
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.21203/rs.3.rs-1440760/v1