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Explorations in quantum machine learning

By:

Khandelwal, Ankit

Contributor(s):

advised by Patel, Apoorva D

Material type: Book

BookPublication details: Bangalore: Indian Institute of Science, 2023.Description: xxi, 135p.: col. ill. e-Thesis 5.787 MBDissertation: MS; 2023; Centre for High Energy PhysicsSubject(s):

DDC classification:

530.12 KHA

Online resources:

Click here to access online

Dissertation note: MS; 2023; Centre for High Energy Physics Summary: Quantum Machine Learning is a rapidly developing field. In part 1 of this thesis, I benchmark common QML methods with the most popular datasets used in classical ML. I compare results from quantum and classical methods and provide detailed graphs and data which others can use in the future to compare new models. In part 2 of the thesis, I pick up the kicked top model and frame it as a classification problem. This is used as an example of using quantum data with QML models. We can even achieve 100% accuracy with specific parameters and initial conditions. In the last part, I also take a look at how noise affects our results which is important in the NISQ era and also how the loss of information can reduce the performance but can still provide usable results.

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Holdings
Item type	Current library	Call number	URL	Status	Date due	Barcode
Thesis	JRD Tata Memorial Library	530.12 KHA (Browse shelf(Opens below))	Link to resource	Available		ET00343

includes bibliographical references and index

MS; 2023; Centre for High Energy Physics

Quantum Machine Learning is a rapidly developing field. In part 1 of this thesis, I benchmark common QML methods with the most popular datasets used in classical ML. I compare results from quantum and classical methods and provide detailed graphs and data which others can use in the future to compare new models. In part 2 of the thesis, I pick up the kicked top model and frame it as a classification problem. This is used as an example of using quantum data with QML models. We can even achieve 100% accuracy with specific parameters and initial conditions. In the last part, I also take a look at how noise affects our results which is important in the NISQ era and also how the loss of information can reduce the performance but can still provide usable results.

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