Machine learning techniques in particle physics are most powerful when they are trained directly on data, to avoid sensitivity to theoretical uncertainties or an underlying bias on the expected signal. To be able to train on data in searches for new physics, anomaly detection methods are imperative, which can be realised by an autoencoder acting as an unsupervised classifier. The last source of uncertainties affecting the classifier are then experimental uncertainties in the reconstruction of the final-state objects. To mitigate their effect on the classifier and to allow for a realistic assessment of the method, we propose to combine the autoencoder with an adversarial neural network to remove its sensitivity to the smearing of the final-state objects. We quantify its effect and show that one can achieve a robust anomaly detection in resonance-induced tt final states. * If machine learning techniques can be trained on data directly they become independent of theoretical uncertainties. In such circumstances they can outperform theory-based reconstruction approaches, like the matrix element method [44][45][46][47][48], which was recently extended to fully exclusive final states [49][50][51][52][53][54].† The quantum numbers of the decaying resonances are known to have a strong impact on the reconstruction efficiencies of boosted top quarks [61].

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.

Photonic Quantum Computers provide several benefits over the discrete qubit-based paradigm of quantum computing. By using the power of continuous-variable computing we build an anomaly detection model to use on searches for New Physics. Our model uses Gaussian Boson Sampling, a #P-hard problem and thus not efficiently accessible to classical devices. This is used to create feature vectors from graph data, a natural format for representing data of high-energy collision events. A simple K-means clustering algorithm is used to provide a baseline method of classification. We then present a novel method of anomaly detection, combining the use of Gaussian Boson Sampling and a quantum extension to K-means known as Q-means. This is found to give equivalent results compared to the classical clustering version while also reducing the $$ \mathcal{O} $$ O complexity, with respect to the sample’s feature-vector length, from $$ \mathcal{O}(N) $$ O N to $$ \mathcal{O}\left(\log (N)\right) $$ O log N .

We study the phenomenology of light scalars of masses m1 and m2 coupling to heavy flavourviolating vector bosons of mass mV . For m1,2 few GeV, this scenario triggers the rare B meson decays B 0the last two being the most important ones for m1 ∼ m2. None of these signals has been studied experimentally; therefore we propose analyses to test these channels at the LHCb. We demonstrate that the reach of this facility extends to branching ratios as small as 6.0×10 −9 , 1.6×10 −9 , 5.9×10 −9 and 1.8×10 −8 for the aforementioned channels, respectively. For m1,2 O(1) GeV, we show that slightly modified versions of current multilepton and multitau searches at the LHC can probe wide regions of the parameter space of this scenario. Altogether, the potential of the searches we propose outperform other constraints such as those from meson mixing.

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