Preparation of Quantum Machine Learning Datasets with Quantum Advantage and Challenges using State-of-the-art Classical Machine Learning

Machine Learning (ML) approaches generally consist of training an algorithm on a given dataset containing data which has to be analyzed or otherwise understood. For an ML application to be successful, careful thought must be given to ensuring that the architecture of the algorithm chosen is fit for the task at hand: some architectures are tailored for sequential data (stock market data, audio data, etc.) while others are tailored for image data. One subset of ML algorithms is Quantum Machine Learning, which seeks to utilize quantum computing techniques. This research project aims to select a set of quantum datasets and evaluate the performance of both quantum and traditional ML algorithms on them, in order to demonstrate that quantum machine learning can outperform classical machine learning methods on certain tasks of interest, such as classifying quantum circuits. The expected outcomes of this research are to advance the field of quantum machine learning and to lay the groundwork for future work in this area.

Faculty Supervisor:

Arthur Chan

Student:

Partner:

Osaka University

Discipline:

Computer science

Sector:

Education

University:

University of Toronto

Program: