Identifying minimum-energy structures of lattice proteins is a challenging discrete optimization problem. Quantum approaches such as analog quantum annealing and the gate-based quantum approximate optimization algorithm (QAOA) can address this problem after mapping it to a binary representation, which typically involves introducing penalty terms to enforce valid chain configurations. However, in this and many related problems, the use of quadratic penalty terms can be avoided by restricting the search space to independent sets in a conflict graph and using a QAOA mixer designed for the maximum independent set problem. In this work, we implement and explore this QAOA variant for lattice protein folding. Here, the objective function consists solely of the protein energy together with a simple linear bias term, without quadratic penalties. We validate this approach through classical simulations of the quantum circuits for lattice proteins of lengths N=4 and N=6. To explore larger systems, we further introduce a heuristic iterative local-search scheme, with which we successfully fold lattice proteins with lengths up to N=14 using local subgraphs with at most 26 qubits.

We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator development lead to a more comprehensive understanding of physics at the highest energies and intensities, and allow models to be tested against a wealth of data that have been accumulated over the past decades. A cohesive approach to event generator development will allow these models to be further improved and systematic uncertainties to be reduced, directly contributing to future experimental success. Event generators are part of a much larger ecosystem of computational tools. They typically involve a number of unknown model parameters that must be tuned to experimental data, while maintaining the integrity of the underlying physics models. Making both these data, and the analyses with which they have been obtained accessible to future users is an essential aspect of open science and data preservation. It ensures the consistency of physics models across a variety of experiments.

The capabilities of quantum computers, such as the number of supported qubits and maximum circuit depth, have grown exponentially in recent years. Commercially relevant applications that take advantage of quantum computing are expected to be available soon. In this paper, we shed light on the possibilities of accelerating database tasks using quantum computing with examples of optimizing queries and transaction schedules and present some open challenges for future studies in the field.

Several benchmarks have been proposed to holistically measure quantum computing performance. While some have focused on the end user's perspective (e.g., in application-oriented benchmarks), the real industrial value taking into account the physical footprint of the quantum processor are not discussed. Different use-cases come with different requirements for size, weight, power consumption, or data privacy while demanding to surpass certain thresholds of fidelity, speed, problem size, or precision. This paper aims to incorporate these characteristics into a concept coined quantum utility, which demonstrates the effectiveness and practicality of quantum computers for various applications where quantum advantage - defined as either being faster, more accurate, or demanding less energy - is achieved over a classical machine of similar size, weight, and cost. To successively pursue quantum utility, a level-based classification scheme - constituted as application readiness levels (ARLs) as well as extended classification labels are introduced. These are demonstratively applied to different quantum applications from the fields of quantum chemistry, quantum simulation, quantum machine learning, and data analysis followed by a brief discussion.

Streams of event weights in particle-level Monte Carlo event generators are a convenient and immensely CPU-efficient approach to express systematic uncertainties in phenomenology calculations, providing systematic variations on the nominal prediction within a single event sample. But the lack of a common standard for labelling these variation streams across different tools has proven to be a major limitation for event-processing tools and analysers alike. Here we propose a well-defined, extensible community standard for the naming, ordering, and interpretation of weight streams that will serve as the basis for semantically correct parsing and combination of such variations in both theoretical and experimental studies.

We demonstrate - for the first time - the application of a quantum machine learning (QML) algorithm on an on-site room-temperature quantum computer. A two-qubit quantum computer installed at the Pawsey Supercomputing Centre in Perth, Australia, is used to solve multi-class classification problems on unseen, i.e. untrained, 2D data points. The underlying 1-qubit model is based on the data re-uploading framework of the universal quantum classifier and was trained on an ideal quantum simulator using the Adam optimiser. No noise models or device-specific insights were used in the training process. The optimised model was deployed to the quantum device by means of a single XYX decomposition leading to three parameterised single qubit rotations. The results for different classification problems are compared to the optimal results of an ideal simulator. The room-temperature quantum computer achieves very high classification accuracies, on par with ideal state vector simulations.

Abstract Azimuthal correlations in $$\mathrm {Z} +$$ Z + jet production at large transverse momenta are computed by matching Parton-Branching (PB) TMD parton distributions and showers with NLO calculations via MCatNLO. The predictions are compared with those for dijet production in the same kinematic range. The azimuthal correlations $$\Delta \phi $$ Δ ϕ between the Z boson and the leading jet are steeper compared to those in dijet production at transverse momenta $$\mathcal{O}(100)$$ O ( 100 ) GeV , while they become similar for very high transverse momenta $${{\mathcal {O}}}(1000)$$ O ( 1000 ) GeV . The different patterns of $$\mathrm {Z} +$$ Z + jet and dijet azimuthal correlations can be used to search for potential factorization-breaking effects in the back-to-back region, which depend on the different color and spin structure of the final states and their interferences with the initial states. In order to investigate these effects experimentally, we propose to measure the ratio of the distributions in $$\Delta \phi $$ Δ ϕ for $$\mathrm {Z} +$$ Z + jet- and multijet production at low and at high transverse momenta, and compare the results to predictions obtained assuming factorization. We examine the role of theoretical uncertainties by performing variations of the factorization scale, renormalization scale and matching scale. In particular, we present a comparative study of matching scale uncertainties in the cases of PB-TMD and collinear parton showers.

We introduce a method for the separation of soft and collinear logarithms in QCD parton evolution at $\mathcal{O}(\alpha_s^2)$ and at leading color. Using an implementation of the technique in the Dire parton shower, we analyze the numerical impact of genuine triple-collinear corrections from quark pair emission in $e^+e^-\to$ hadrons.

The azimuthal correlation, Δϕ _12 , of high transverse momentum jets in pp collisions at √(s)=13 TeV is studied by applying PB-TMD distributions to NLO calculations via MCatNLO together with the PB-TMD parton shower. A very good description of the cross section as a function of Δϕ _12 is observed. In the back-to-back region of Δϕ _12→π , a very good agreement is observed with the PB-TMD Set 2 distributions while significant deviations are obtained with the PB-TMD Set 1 distributions. Set 1 uses the evolution scale while Set 2 uses transverse momentum as an argument in α _s , and the above observation therefore confirms the importance of an appropriate soft-gluon coupling in angular ordered parton evolution. The total uncertainties of the predictions are dominated by the scale uncertainties of the matrix element, while the uncertainties coming from the PB-TMDs and the corresponding PB-TMD shower are very small. The Δϕ _12 measurements are also compared with predictions using MCatNLO together Pythia 8, illustrating the importance of details of the parton shower evolution.

Detailed and precise background predictions are the backbone of large parts of high-energy collider phenomenology. This requires to embed precision QCD calculations into detailed event generators, to produce comprehensive software simulations. Only continued progress in this direction will allow us to exploit the full potential of measurements at the Large Hadron Collider, or at a future ElectronIon Collider. This work presents a method to combine third-order QCD calculations for hadronic scattering processes with Monte-Carlo event generators, thus enabling a new generation of precision predictions.

Electron Ion Collider (EIC) is a particle accelerator facility planned for construction at Brookhaven National Laboratory on Long Island, New York by the United States Department of Energy. EIC will provide capabilities of colliding beams of polarized electrons with polarized beams of proton and light ions. EIC will be one of the largest and most sophisticated new accelerator facilities worldwide, and the only new large-scale accelerator facility planned for construction in the United States in the next few decades. The versatility, resolving power and intensity of EIC will present many new opportunities to address some of the crucial and fundamental open scientific questions in particle physics. This document provides an overview of the science case of EIC from the perspective of the high energy physics community.

We discuss our recent results on azimuthal distributions in vector boson + jets and multi-jet production at the LHC, obtained from the matching of next-to-leading order (NLO) perturbative matrix elements with transverse momentum dependent (TMD) parton branching. We present a comparative analysis of boson-jet and jet-jet correlations in the back to-back region, and a study of the theoretical systematic uncertainties associated with the matching scale in the cases of TMD and collinear parton showers.

High-quality simulated data is crucial for particle physics discoveries. Therefore, parton shower algorithms are a major building block of the data synthesis in event generator programs. However, the core algorithms used to generate parton showers have barely changed since the 1980s. With quantum computers' rapid and continuous development, dedicated algorithms are required to exploit the potential that quantum computers provide to address problems in high-energy physics. This paper presents a novel approach to synthesising parton showers using the Discrete QCD method. The algorithm benefits from an elegant quantum walk implementation which can be embedded into the classical toolchain. We use the ibm_algiers device to sample parton shower configurations and generate data that we compare against measurements taken at the ALEPH, DELPHI and OPAL experiments. This is the first time a Noisy Intermediate-Scale Quantum (NISQ) device has been used to simulate realistic high-energy particle collision events.

We develop EventMover, a differentiable parton shower event generator. This tool generates high- and variable-length scattering events that can be moved with simulation derivatives to change the value of the scale $\Lambda_\mathrm{QCD}$ defining the strong coupling constant, without introducing statistical variations between samples. To demonstrate the potential for EventMover, we compare the output of the simulation with $e^+e^-$ data to show how one could fit $\Lambda_\mathrm{QCD}$ with only a single event sample. This is a critical step towards a fully differentiable event generator for particle and nuclear physics.

Parton showers are crucial components of high-energy physics calculations. Improving their modelling of QCD is an active research area since shower approximations are stumbling blocks for precision event generators. Naively, the interference between sub-dominant Standard-Model interactions and QCD can be of similar size to subleading QCD corrections. This article assesses the impact of QCD/QED interference effects in parton showers, by developing a sophisticated shower including QED, QCD at fixed color, and employing complete tree-level matrix element corrections for individual $N_C=3$ color configurations to embed interference. The resulting simulation indicates that QCD/QED interference effects are small for a simple test case and dwarfed by electro-weak resonance effects.

This manual describes the PYTHIA 8.3 event generator, the most recent version of an evolving physics tool used to answer fundamental questions in particle physics. The program is most often used to generate high-energy-physics collision"events", i.e. sets of particles produced in association with the collision of two incoming high-energy particles, but has several uses beyond that. The guiding philosophy is to produce and reproduce properties of experimentally obtained collisions as accurately as possible. The program includes a wide ranges of reactions within and beyond the Standard Model, and extending to heavy ion physics. Emphasis is put on phenomena where strong interactions play a major role. The manual contains both pedagogical and practical components. All included physics models are described in enough detail to allow the user to obtain a cursory overview of used assumptions and approximations, enabling an informed evaluation of the program output. A number of the most central algorithms are described in enough detail that the main results of the program can be reproduced independently, allowing further development of existing models or the addition of new ones. Finally, a chapter dedicated fully to the user is included towards the end, providing pedagogical examples of standard use cases, and a detailed description of a number of external interfaces. The program code, the online manual, and the latest version of this print manual can be found on the PYTHIA web page: https://www.pythia.org/

The constituents of dark matter are still unknown, and the viable possibilities span a vast range of masses. The physics community has established searching for sub-GeV dark matter as a high priority and identified accelerator-based experiments as an essential facet of this search strategy. A key goal of the accelerator-based dark matter program is testing the broad idea of thermally produced sub-GeV dark matter through experiments designed to directly produce dark matter particles. The most sensitive way to search for the production of light dark matter is to use a primary electron beam to produce it in fixed-target collisions. The Light Dark Matter eXperiment (LDMX) is an electron-beam fixed-target missing-momentum experiment that realizes this approach and provides unique sensitivity to light dark matter in the sub-GeV range. This contribution provides an overview of the theoretical motivation, the main experimental challenges, how LDMX addresses these challenges, and projected sensitivities. We further describe the capabilities of LDMX to explore other interesting new and standard physics, such as visibly-decaying axion and vector mediators or rare meson decays, and to provide timely electronuclear scattering measurements that will inform the modeling of neutrino-nucleus scattering for DUNE.

Discriminating between quark- and gluon-initiated jets has long been a central focus of jet substructure, leading to the introduction of numerous observables and calculations to high perturbative accuracy. At the same time, there have been many attempts to fully exploit the jet radiation pattern using tools from statistics and machine learning. We propose a new approach that combines a deep analytic understanding of jet substructure with the optimality promised by machine learning and statistics. After specifying an approximation to the full emission phase space, we show how to construct the optimal observable for a given classification task. This procedure is demonstrated for the case of quark and gluons jets, where we show how to systematically capture sub-eikonal corrections in the splitting functions, and prove that linear combinations of weighted multiplicity is the optimal observable. In addition to providing a new and powerful framework for systematically improving jet substructure observables, we demonstrate the performance of several quark versus gluon jet tagging observables in parton-level Monte Carlo simulations, and find that they perform at or near the level of a deep neural network classifier. Combined with the rapid recent progress in the development of higher order parton showers, we believe that our approach provides a basis for systematically exploiting subleading effects in jet substructure analyses at the Large Hadron Collider (LHC) and beyond.

This manual describes the Pythia event generator, the most recent version of an evolving physics tool used to answer fundamental questions in particle physics. The program is most often used to generate high-energy-physics collision “events”, i.e. sets of particles produced in association with the collision of two incoming high-energy particles, but has several uses beyond that. The guiding philosophy is to produce and re-produce properties of experimentally obtained collisions as accurately as possible. The program includes a wide ranges of reactions within and beyond the Standard Model, and extending to heavy ion physics. Emphasis is put on phenomena where strong interactions play a major role. The manual contains both pedagogical and practical components. All included physics models are described in enough detail to allow the user to obtain a cursory overview of used assumptions and approximations, enabling an informed evaluation of the program output. A number of the most central algorithms are described in enough detail that the main results of the program can be reproduced independently, allowing further development of existing models or the addition of new ones. Finally, a chapter dedicated fully to the user is included towards the end, providing pedagogical examples of standard use cases, and a detailed description of a number of external interfaces. The program code, the online manual, and the latest version of this print manual can be found on the Pythia web page: https://www.pythia.org/.

Models where dark matter is a part of an electroweak multiplet feature charged particles with macroscopic lifetimes due to the charged-neutral mass split of the order of pion mass. At the Large Hadron Collider, the ATLAS and CMS experiments will identify these charged particles as disappearing tracks, since they decay into a massive invisible dark matter candidate and a very soft charged Standard-Model particle which fails to pass the reconstruction requirements. While ATLAS and CMS have focused on the supersymmetric versions of these scenarios, we have performed here the reinterpretation of the latest ATLAS disappearing track search for a suite of dark matter multiplets with different spins and electroweak quantum numbers. More concretely, we consider the cases of the inert Two Higgs Doublet model (i2HDM), of Minimal Fermion Dark Matter (MFDM) and of Vector Triplet Dark Matter (VTDM). Our procedure is validated by using the same wino and higgsino benchmark models employed by the ATLAS collaboration. We have found that with the disappearing track signature one can probe a vast portion of the parameter space, well beyond the reach of prompt missing energy searches (notably mono-jets). We provide tables with the upper-limits on the cross-section upper limits, and efficiencies in the lifetime - dark matter mass plane for all the models under consideration. Moreover we make the recasting code employed here publicly available, as part of the LLP Recasting Repository.