  Quantum machine learning aims to release the prowess of quantum computing to
improve machine learning methods. By combining quantum computing methods with
classical neural network techniques we aim to foster an increase of performance
in solving classification problems. Our algorithm is designed for existing and
near-term quantum devices. We propose a novel hybrid variational quantum
classifier that combines the quantum gradient descent method with steepest
gradient descent to optimise the parameters of the network. By applying this
algorithm to a resonance search in di-top final states, we find that this
method has a better learning outcome than a classical neural network or a
quantum machine learning method trained with a non-quantum optimisation method.
The classifiers ability to be trained on small amounts of data indicates its
benefits in data-driven classification problems.
