Binary classifiers for noisy datasets: A comparative study of existing quantum machine learning frameworks and some new approaches

• Main Authors: SCHETAKIS, Nikolaos, AGHAMALYAN, Davit, GRIFFIN, Paul Robert, BOGUSLAVSKY, Michael
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Quantum; Binary Classifiers; Machine learning; AI; Databases and Information Systems; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7738; https://ink.library.smu.edu.sg/context/sis_research/article/8741/viewcontent/binary_classifiers_for_noisy_datasets_a_comparative_study_of_existing_quantum_machine_learning_frameworks_and_some_new_approaches.pdf
• Institution: Singapore Management University

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.