  The recent physical realisation of quantum computers with dozens to hundreds
of noisy qubits has given birth to an intense search for useful applications of
their unique capabilities. One area that has received particular attention is
quantum machine learning (QML), the study of machine learning algorithms
running natively on quantum computers. Such algorithms have begun to be applied
to data intensive problems in particle physics, driven by the expected
increased capacity for pattern recognition of quantum computers. In this work
we develop and apply QML methods to B meson flavour tagging, an important
component of experiments in particle physics which probe heavy quark mixing and
CP violation in order to obtain a better understanding of the matter-antimatter
asymmetry observed in the universe. We simulate boosted ensembles of quantum
support vector machines (QSVMs) based on both conventional qubit-based and
continuous variable architectures, attaining effective tagging efficiencies of
28.0% and 29.2% respectively, comparable with the leading published result of
30.0% using classical machine learning algorithms. The ensemble nature of our
classifier is of particular importance, doubling the effective tagging
efficiency of a single QSVM, which we find to be highly prone to overfitting.
These results are obtained despite the strong constraint of working with QSVM
architectures that are classically simulable, and we find evidence that
continuous variable QSVMs beyond the classically simulable regime may be able
to realise even higher performance, surpassing the reported classical results,
when sufficiently powerful quantum hardware is developed to execute them.
