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...