Quantum binary classifiers for noisy datasets
Publication Type
Video
Publication Date
9-2021
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. Presented at TechInnovation, Singapore, 28-30 September 2021.
Keywords
Quantum, Binary Classifiers, Machine learning
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Information Systems and Management
Publisher
Singapore Press Holdings
Citation
GRIFFIN, Paul Robert; SCHETAKIS, Nikolaos; and AGHAMALYAN, Davit.
Quantum binary classifiers for noisy datasets. (2021).
Available at: https://ink.library.smu.edu.sg/sis_research/9912
