Quantum Training of Neural Networks

We are witnessing an explosion in the use of machine learning (ML) algorithms with significant impacts on the world’s economic and social activities. The backbone of a machine learning algorithm is a deep neural network which is composed of hundreds to thousands of neurons. To make the neural networks (NNs) functional, they need to be trained using a training dataset. It is known that the training of the networks is computationally intensive and may take several weeks or even months to complete. In this research project, we aim to develop a quantum program to train classical neural networks. The core of a training algorithm is matrix multiplication which is a linear operation and can be implemented in a quantum computer. This research project opens up new avenues for further research in the area of quantum computing and enables deployment of more accurate ML algorithms for real world applications.

Faculty Supervisor:

Ehsan Atoofian

Student:

Partner:

CMC Microsystems

Discipline:

Engineering

Sector:

Artificial Intelligence; Technology; Information and Communications Technology; Quantum Science

University:

Lakehead University

Program: