  Quantum computers have the potential for significant speed-ups of certain
computational tasks. A possibility this opens up within the field of machine
learning is the use of quantum features that would be inefficient to calculate
classically. Machine learning algorithms are ubiquitous in particle physics and
as advances are made in quantum machine learning technology, there may be a
similar adoption of these quantum techniques. In this work a quantum support
vector machine (QSVM) is implemented for signal-background classification. We
investigate the effect of different quantum encoding circuits, the process that
transforms classical data into a quantum state, on the final classification
performance. We show an encoding approach that achieves an Area Under Receiver
Operating Characteristic Curve (AUC) of 0.877 determined using quantum circuit
simulations. For this same dataset the best classical method, a classical
Support Vector Machine (SVM) using the Radial Basis Function (RBF) Kernel
achieved an AUC of 0.865. Using a reduced dataset we then ran the algorithm on
the IBM Quantum ibmq_casablanca device achieving an average AUC of 0.703. As
further improvements to the error rates and availability of quantum computers
materialise, they could form a new approach for data analysis in high energy
physics.
