Gradient-free methods for optimising quantum machine learning methods

Project Level: Honours, PhD

Quantum machine learning is an emerging field that has the potential to leverage the power of quantum computers for the task of learning from data. One fundamental problem in training quantum machine learning is that there is no equivalent of the backpropagation process of the classical neural networks to calculate the gradient efficiently. In this project, we investigate the ability of different gradient-free optimisation algorithms in training quantum machine learning networks and work on ways to alter them to suit these quantum learning scenarios.