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

Additional URL

https://doi.org/10.21203/rs.3.rs-1440760/v1

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