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dc.contributor.advisorShahriar, Shadman
dc.contributor.authorKhan, Nuraiya Rahman
dc.date.accessioned2025-02-05T04:22:24Z
dc.date.available2025-02-05T04:22:24Z
dc.date.copyright©2024
dc.date.issued2024-05
dc.identifier.otherID 18301174
dc.identifier.urihttp://hdl.handle.net/10361/25315
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 32-33).
dc.description.abstractThe goal of this research is to introduce the non-practicing reader to the new discipline of quantum machine learning, which merges the machine learning and quantum computing fields, as well as to the emerging topic of quantum mechanical learning. is. In order to provide you a deeper grasp of the most recent quantum machine learning approaches, this paper discusses quantum machine learning from the fundamentals of quantum logic to some specific quantum computing elements and algorithms. Then, utilizing the most recent quantum machine learning techniques, we discuss challenges with drug discovery and cover fundamental aspects of quantum machine learning, including in-depth explanations of some well-known algorithms.en_US
dc.description.statementofresponsibilityNuraiya Rahman Khan
dc.format.extent40 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectDrug discoveryen_US
dc.subjectMachine learningen_US
dc.subjectQNNen_US
dc.subjectQuantum computingen_US
dc.subject.lcshQuantum computers.
dc.subject.lcshDrugs--Design.
dc.subject.lcshPharmaceutical technology.
dc.subject.lcshDrug development.
dc.subject.lcshMachine learning--Medical applications.
dc.titleUtilizing quantum machine learning for efficient drug discoveryen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB.Sc. in Computer Science and Engineering


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