Training neural networks with universal adiabatic quantum computing.

The training of neural networks (NNs) is a computationally intensive task requiring significant time and resources. This article presents a novel approach to NN training using adiabatic quantum computing (AQC), a paradigm that leverages the principles of adiabatic evolution to solve optimization problems. We propose a universal AQC method that can be implemented on gate quantum computers, allowing for a broad range of Hamiltonians and thus enabling the training of expressive neural networks. We apply this approach to various neural networks with continuous, discrete, and binary weights. The study results indicate that AQC can very efficiently evaluate the global minimum of the loss function, offering a promising alternative to classical training methods.

Quantum pathways for charged track finding in high-energy collisions.

In high-energy particle collisions, charged track finding is a complex yet crucial endeavor. We propose a quantum algorithm, specifically quantum template matching, to enhance the accuracy and efficiency of track finding. Abstracting the Quantum Amplitude Amplification routine by introducing a data register, and utilizing a novel oracle construction, allows data to be parsed to the circuit and matched with a hit-pattern template, without prior knowledge of the input data. Furthermore, we address the challenges posed by missing hit data, demonstrating the ability of the quantum template matching algorithm to successfully identify charged-particle tracks from hit patterns with missing hits. Our findings therefore propose quantum methodologies tailored for real-world applications and underline the potential of quantum computing in collider physics.

Three-Body Entanglement in Particle Decays.

Quantum entanglement has long served as a foundational pillar in understanding quantum mechanics, with a predominant focus on two-particle systems. We extend the study of entanglement into the realm of three-body decays, offering a more intricate understanding of quantum correlations. We introduce a novel approach for three-particle systems by utilizing the principles of entanglement monotone concurrence and the monogamy property. Our findings highlight the potential of studying deviations from the standard model and emphasize its significance in particle phenomenology. This work paves the way for new insights into particle physics through multiparticle quantum entanglement, particularly in decays of heavy fermions and hadrons.

Simulating anti-skyrmions on a lattice.

Magnetic skyrmions are meta-stable spin structures that naturally emerge in magnetic materials. While a vast amount of effort has gone into the study of their properties, their counterpart of opposite topological charge, the anti-skyrmion, has not received as much attention. We aim to close this gap by deploying Monte Carlo simulations of spin-lattice systems in order to investigate which interactions support anti-skyrmions, as well as skyrmions of Bloch and Néel type. We find that the combination of ferromagnetic exchange and Dzyaloshinskii-Moriya (DM) interactions is able to stabilize each of the three types, depending on the specific structure of the DM interactions. Considering a three-dimensional spin lattice model, we provide a finite-temperature phase diagram featuring a stable anti-skyrmion lattice phase for a large range of temperatures. In addition, we also shed light on the creation and annihilation processes of these anti-skyrmion tubes and study the effects of the DM interaction strength on their typical size.

IRC-Safe Graph Autoencoder for Unsupervised Anomaly Detection.

Anomaly detection through employing machine learning techniques has emerged as a novel powerful tool in the search for new physics beyond the Standard Model. Historically similar to the development of jet observables, theoretical consistency has not always assumed a central role in the fast development of algorithms and neural network architectures. In this work, we construct an infrared and collinear safe autoencoder based on graph neural networks by employing energy-weighted message passing. We demonstrate that whilst this approach has theoretically favorable properties, it also exhibits formidable sensitivity to non-QCD structures.

Machine learning uncertainties with adversarial neural networks.

Machine Learning is a powerful tool to reveal and exploit correlations in a multi-dimensional parameter space. Making predictions from such correlations is a highly non-trivial task, in particular when the details of the underlying dynamics of a theoretical model are not fully understood. Using adversarial networks, we include a priori known sources of systematic and theoretical uncertainties during the training. This paves the way to a more reliable event classification on an event-by-event basis, as well as novel approaches to perform parameter fits of particle physics data. We demonstrate the benefits of the method explicitly in an example considering effective field theory extensions of Higgs boson production in association with jets.

h h + Jet production at 100 TeV.

Higgs pair production is a crucial phenomenological process in deciphering the nature of the TeV scale and the mechanism underlying electroweak symmetry breaking. At the Large Hadron Collider, this process is statistically limited. Pushing the energy frontier beyond the LHC's reach will create new opportunities to exploit the rich phenomenology at higher centre-of-mass energies and luminosities. In this work, we perform a comparative analysis of the h h + jet channel at a future 100 TeV hadron collider. We focus on the h h → b b ¯ b b ¯ and h h → b b ¯ τ + τ - channels and employ a range of analysis techniques to estimate the sensitivity potential that can be gained by including this jet-associated Higgs pair production to the list of sensitive collider processes in such an environment. In particular, we observe that h h → b b ¯ τ + τ - in the boosted regime exhibits a large sensitivity to the Higgs boson self-coupling and the Higgs self-coupling could be constrained at the 8% level in this channel alone.

Jet-associated resonance spectroscopy.

We present a model-independent study aimed at characterising the nature of possible resonances in the jet-photon or jet-Z final state at hadron colliders. Such resonances are expected in many models of compositeness and would be a clear indication of new physics. At leading order, in the narrow width approximation, the matrix elements are parameterised by just a few constants describing the coupling of the various helicities to the resonance. We present the full structure of such amplitudes up to spin 2 and use them to simulate relevant kinematic distributions that could serve to constrain the coupling structure. This also generalises the signal generation strategy that is currently pursued by ATLAS and CMS to the most general case in the considered channels. While the determination of the P/CP properties of the interaction seems to be out of reach within this framework, there is a wealth of information to be gained about the spin of the resonance and the relative couplings of the helicities.

Production of hhjj at the LHC.

Until now, a phenomenologically complete analysis of the hh+2j channel at the LHC has been missing. This is mostly due to the high complexity of the involved one-loop gluon fusion contribution and the fact that a reliable estimate thereof cannot be obtained through simplified calculations in the mt→∞ limit. In this Letter, we report on the LHC's potential to access di-Higgs production in association with two jets in a fully showered hadron-level analysis. Our study includes the finite top and bottom mass dependencies for the gluon fusion contribution.

Fat jets for a light higgs boson.

At the LHC associated top quark and Higgs boson production with a Higgs boson decay to bottom quarks has long been a heavily disputed search channel. Recently, it has been found not to be viable. We show how it can be observed by tagging massive Higgs bosons and top jets. For this purpose we construct boosted top and Higgs taggers for standard-model processes in a complex QCD environment.