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We introduce a hybrid quantum-classical vision transformer architecture, notable for its integration of variational quantum circuits within both the attention mechanism and the multi-layer perceptrons. The research addresses the critical challenge of computational efficiency and resource constraints in analyzing data from the upcoming High Luminosity Large Hadron Collider, presenting the architecture as a potential solution. In particular, we evaluate our method by applying the model to multi-detector jet images from CMS Open Data. The goal is to distinguish quark-initiated from gluon-initiated jets. We successfully train the quantum model and evaluate it via numerical simulations. Using this approach, we achieve classification performance almost on par with the one obtained with the completely classical architecture, considering a similar number of parameters., Comment: 14 pages, 8 figures. Published in MDPI Axioms 2024, 13(5), 323

We compute the suppression of bottomonium in the quark-gluon plasma using the three-loop QCD static potential. The potential describes the spin-averaged bottomonium spectrum below threshold with a less than 1% error. Within potential nonrelativistic quantum chromodynamics and an open quantum systems framework, we compute the evolution of the bottomonium density matrix. The values of the quarkonium transport coefficients are obtained from lattice QCD measurements of the bottomonium in-medium width and thermal mass shift; we additionally include for the first time a vacuum contribution to the dispersive coefficient ${\gamma}$. Using the three-loop potential and the values of the heavy quarkonium transport coefficients, we find that the resulting bottomonium nuclear modification factor is consistent with experimental observations, while at the same time reproducing the lattice measurements of the in-medium width., Comment: 15 pages, 3 figures, 1 table

Models based on vision transformer architectures are considered state-of-the-art when it comes to image classification tasks. However, they require extensive computational resources both for training and deployment. The problem is exacerbated as the amount and complexity of the data increases. Quantum-based vision transformer models could potentially alleviate this issue by reducing the training and operating time while maintaining the same predictive power. Although current quantum computers are not yet able to perform high-dimensional tasks yet, they do offer one of the most efficient solutions for the future. In this work, we construct several variations of a quantum hybrid vision transformer for a classification problem in high energy physics (distinguishing photons and electrons in the electromagnetic calorimeter). We test them against classical vision transformer architectures. Our findings indicate that the hybrid models can achieve comparable performance to their classical analogues with a similar number of parameters., Comment: 13 pages, 9 figures. Published version in a special issue "Computational Aspects of Machine Learning and Quantum Computing"

This paper presents a comprehensive comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNN) and Quantum Neural Networks (QNN), juxtaposed against their classical counterparts: Equivariant Neural Networks (ENN) and Deep Neural Networks (DNN). We evaluate the performance of each network with two toy examples for a binary classification task, focusing on model complexity (measured by the number of parameters) and the size of the training data set. Our results show that the $\mathbb{Z}_2\times \mathbb{Z}_2$ EQNN and the QNN provide superior performance for smaller parameter sets and modest training data samples., Comment: 13 pages, 7 figures

Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with graph structures. Therefore, deep geometric methods, such as graph neural networks (GNNs), have been leveraged for various data analysis tasks in high-energy physics. One typical task is jet tagging, where jets are viewed as point clouds with distinct features and edge connections between their constituent particles. The increasing size and complexity of the LHC particle datasets, as well as the computational models used for their analysis, greatly motivate the development of alternative fast and efficient computational paradigms such as quantum computation. In addition, to enhance the validity and robustness of deep networks, one can leverage the fundamental symmetries present in the data through the use of invariant inputs and equivariant layers. In this paper, we perform a fair and comprehensive comparison between classical graph neural networks (GNNs) and equivariant graph neural networks (EGNNs) and their quantum counterparts: quantum graph neural networks (QGNNs) and equivariant quantum graph neural networks (EQGNN). The four architectures were benchmarked on a binary classification task to classify the parton-level particle initiating the jet. Based on their AUC scores, the quantum networks were shown to outperform the classical networks. However, seeing the computational advantage of the quantum networks in practice may have to wait for the further development of quantum technology and its associated APIs., Comment: 15 pages, 7 figures, 3 appendices

Rare $\vert \Delta c \vert=\vert \Delta u \vert=1$ processes uniquely probe flavor in the Standard Model and beyond from the up-type quark sector. Opportunities to search for BSM physics in charm arise from the severe GIM suppression which kills SM contributions to leptonic axial-vector contributions and suppresses CP violation. Semileptonic decays of charmed hadrons offer a variety of clean null test observables, featuring new physics effects which are even enhanced by resonance contributions. In particular, angular observables in three- and self-analyzing four-body baryon decays, such as $\Lambda_c\to p \ell^+\ell^-$ and $\Xi_c^+\to\Sigma^+(\to p\pi^0)\ell^+\ell^-$, $\Xi_c^0\to\Lambda^0\,(\to p \pi^-)\ell^+\ell^-$ and $\Omega_c^0\to\Xi^0\,(\to \Lambda^{0} \pi^0)\ell^+\ell^-$ disentangle possible new physics effects in electromagnetic dipole couplings $C_7^{(\prime)}$ and (axial-)vector 4-fermion ones $C^{(\prime)}_{9\,(10)}$. There is sensitivity to BSM couplings as small as $\sim 0.01$., Comment: 6 pages, 3 figures, Contribution to the 11th International Workshop on the CKM Unitarity Triangle (CKM2021), 22-26 November 2021, The University of Melbourne, Australia

We study the full angular distribution of semileptonic rare charm baryon decays in which the secondary baryon undergoes weak decay with sizable polarization parameter, $\Xi_c^+\to\Sigma^+\,(\to p\pi^0)\ell^+\ell^-$, $\Xi_c^0\to\Lambda^0\,(\to p \pi^-)\ell^+\ell^-$ and $\Omega_c^0\to\Xi^0\,(\to \Lambda^{0} \pi^0)\ell^+\ell^-$. Such self-analyzing decay chains allow for seven additional observables compared to three-body decays such as $\Lambda_c \to p \ell^+ \ell^-$, with different sensitivities to the $|\Delta c|=|\Delta u|=1$ weak couplings. Opportunities to test the standard model in $c \to u$ transitions with standard model null tests and other angular observables are worked out. We show that a joint model-independent analysis of the leptonic $A_{\text{FB}}^\ell$, hadronic $A_{\text{FB}}^{\text{H}}$, and combined forward-backward asymmetry $A_{\text{FB}}^{\ell\text{H}}$ together with the fraction of longitudinally produced leptons, $F_L$, is able to pin down the dipole couplings $C_{7},C^\prime_{7}$ and the semileptonic (axial-) vector ones $C_{10},C^\prime_{9},C^\prime_{10}$. $A_{\text{FB}}^{\text{H}}$ is also accessible with dineutrino $c \to u \nu \bar \nu$ modes and probes right-handed currents., Comment: 24 pages, 3 figures, 2 tables

We analyze rare charm baryon decays within the standard model and beyond. We identify all null test observables in unpolarized $\Lambda_c \to p \ell^+ \ell^-$, $\ell=e, \mu$ decays, and study the new physics sensitivities. We find that the longitudinal dilepton polarization fraction $F_L$ is sensitive to electromagnetic dipole couplings $C_{7}^{(\prime)}$, and to the right-handed 4-fermion vector coupling $C_{9}^\prime$. The forward-backward asymmetry, $A_{\rm FB}$, due to the GIM-suppression a standard model null test already, probes the left-handed axialvector 4-fermion coupling $C_{10}$; its CP-asymmetry, $A_{\rm FB}^{\rm CP}$, probes CP-violating phases in $C_{10}$. Physics beyond the standard model can induce branching ratios of dineutrino modes $\Lambda_c \to p \nu \bar \nu $ up to a few times $10^{-5}$, and one order of magnitude smaller if lepton flavor universality is assumed, while standard model rates are negligible. Charged lepton flavor violation allows for striking signals into $e^\pm \mu^\mp$ final states, up to $10^{-6}$ branching ratios model-independently, and up to order $10^{-8}$ in leptoquark models. Related three-body baryon decays $\Xi_c \to \Sigma \ell \ell$, $\Xi_c \to \Lambda \ell \ell$ and $\Omega_c \to \Xi \ell \ell$ offer similar opportunities to test the standard model with $|\Delta c|=|\Delta u|=1$ transitions., Comment: v3: Reference [24] updated, upper left plot in Fig.3 replaced, due to small numerical bug at low-q^2