Investigating the Implementation of Machine Learning Algorithms on Adiabatic Quantum Solvers

Machine learning is an active field of research and development to provide tools and technologies for finding significant patterns in data. Behind every face detection and face recognition software in digital cameras or social network websites a constantly under-development machine learning algorithm is working. Nowadays in any practical applications of machine learning we have to analyze huge amounts of data. Using classical approaches to train machine learning algorithms for some classes of algorithms is either very slow, requiring a lot of computing resources, or inefficient. Quantum computers are promising candidates to deal with computationally difficult problems, which makes development and study of quantum algorithms inevitable. I propose two promising machine learning algorithms that can be developed by drawing on the expertise 1QB Information Technologies has in the development of such algorithms for adiabatic quantum computers, which will speed up the training process in the current version of adiabatic quantum computers, developed by D-Wave Systems Inc.

Faculty Supervisor:

Dr. Mark Schmidt


Hamed Karimi


1QB Information Technologies Inc.




Information and communications technologies


University of British Columbia



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