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The study of the decays of $B$ mesons is a key component of modern experiments which probe heavy quark mixing and $CP$ violation, and may lead to concrete deviations from the predictions of the Standard Model [1]. Flavour tagging, the process of determining the quark flavour composition of $B$ mesons created in entangled pairs at particle accelerators, is an essential component of this analysis, enabling the study of asymmetries in the decay rate of neutral $B$ mesons to flavour agnostic $CP$ eigenstates [1] and the explicit violation of $T$ symmetry at the level of fundamental interactions [2].
Flavour tagging is a difficult problem, depending in general on subtle correlations between the momenta and particle types of the many decay products emerging from the initial particle collision. Problems which require the detection of faint signals within vast quantities of data fall naturally within the domain of machine learning (ML), and indeed flavour tagging has traditionally been most readily tackled via ML [1].
Concurrently, the recent physical realisation of quantum computers has seen significant interest in the prospects of applying quantum machine learning (QML) methods to data intensive problems in particle physics [3]. In this work we employ QML for $B$ meson flavour tagging, investigating the performance of boosted ensembles of continuous variable quantum support vector machines in both the high and low entanglement regimes. We obtain results that are competitive with state-of-the-art classical methods and bode well for the performance of QML algorithms running on the large-scale quantum computers of the future.
[1] Abudinén, F., et al. The European Physical Journal C, 82, (2022)
[2] Lees, J. P., et al. Physical review letters, 109, 211801 (2012)
[3] Heredge, J., et al. Computing and Software for Big Science, 5, (2021)
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