Photonic Quantum Computing for Machine Learning

In the recent years, the advances of machine learning, and more precisely deep learning, have lead to incredible progress in areas such as computer vision, speech recognition and natural language processing. Training algorithms based on deep neural networks can take a huge amount of time and computing resources. The first generation of quantum computers currently being built, although imperfect, is excepted to provide a speed-up over classical computers in several areas including machine learning. One particular paradigm of quantum computing where machine learning algorithms could be accelerated is called Continuous-Variable (CV) ?? or photonic ?? quantum computing. The proposed research project is to identify and implement suitable machine learning algorithms on a photonic quantum computer.

Faculty Supervisor:

Peter Wittek

Student:

Partner:

Université Paris-Saclay

Discipline:

Physics

Sector:

Education

University:

University of Toronto

Program: