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

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

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...

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Bibliographic Details
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
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Institution: Singapore Management University
Language: English

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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

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