Benchmarking fundamental components of quantum machine learning on full-stack photonic quantum computers

Architectures for quantum computing can only scale effectively with suitable benchmarking techniques. Benchmarking is essential for evaluating the performance of quantum computers, including their algorithms and applications. This principle extends to quantum machine learning techniques, where benchmarking basic quantum machine learning methods on full-stack photonic NISQ devices is crucial.
A key challenge involves adapting quantum machine learning algorithms to Quandela’s technology and assessing their performance. Successful benchmarking could yield significant social and economic benefits, enabling the evaluation of quantum computing’s potential in addressing current AI challenges. These challenges include the increasing demand for data, the need for large HPC centers, and high energy consumption. This project aligns with Quandela’s mission to advance quantum technologies and drive meaningful innovation in AI and photonic quantum computing.

Faculty Supervisor:

Guillaume Rabusseau

Student:

Partner:

Quandela

Discipline:

Physics

Sector:

Information and cultural industries

University:

Université de Montréal

Program: