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Adaptation to climate change requires robust climate projections, yet the uncertainty in these projections performed by ensembles of Earth system models (ESMs) remains large. This is mainly due to uncertainties in the representation of subgrid-scale processes such as turbulence or convection that are partly alleviated at higher resolution. New developments in machine learning-based hybrid ESMs demonstrate great potential for systematically reduced errors compared to traditional ESMs. Building on the work of hybrid (physics + AI) ESMs, we here discuss the additional potential of further improving and accelerating climate models with quantum computing. We discuss how quantum computers could accelerate climate models by solving the underlying differential equations faster, how quantum machine learning could better represent subgrid-scale phenomena in ESMs even with currently available noisy intermediate-scale quantum devices, how quantum algorithms aimed at solving optimisation problems could assist in tuning the many parameters in ESMs, a currently time-consuming and challenging process, and how quantum computers could aid in the analysis of climate models. We also discuss hurdles and obstacles facing current quantum computing paradigms. Strong interdisciplinary collaboration between climate scientists and quantum computing experts could help overcome these hurdles and harness the potential of quantum computing for this urgent topic.

Long-term climate projections require running global Earth system models on timescales of hundreds of years and have relatively coarse resolution (from 40 to 160 km in the horizontal) due to their high computational costs. Unresolved subgrid-scale processes, such as clouds, are described in a semi-empirical manner by so called parameterizations, which are a major source of uncertainty in climate projections. Machine learning models trained on short high-resolution climate simulations are promising candidates to replace conventional parameterizations. In this work, we explore the potential of quantum machine learning (QML), and in particular quantum neural networks (QNNs), to develop cloud cover parameterizations. QNNs differ from their classical counterparts, and their potentially high expressivity turns them into promising tools for accurate data-driven schemes to be used in climate models. We perform an extensive comparative analysis between several QNNs and classical neural networks (NNs), by training both ansatzes on data coming from high-resolution simulations with the ICOsahedral Non-hydrostatic weather and climate model (ICON). Our results show that the overall performance of the investigated QNNs is comparable to that of classical NNs of similar size, i.e., with the same number of trainable parameters, with both ansatzes outperforming standard parameterizations used in climate models. Our study includes an analysis of the generalization ability of the models as well as the geometrical properties of their optimization landscape. We also investigate the effects of finite sampling noise, and show that the training and the predictions of the QNNs are stable even in this noisy setting. These results demonstrate the applicability of QML to learn meaningful patterns in climate data, and are thus relevant for a broad range of problems within the climate modeling community.

The RPCs employed at the LHC experiments are currently operated in avalanche mode, with a mixture containing a large fraction of C$_{2}$H$_{2}$F$_{4}$ ($\approx$90\% or more) with the addition of i-C$_{4}$H$_{10}$ and SF$_{6}$ in different concentrations. However, C$_{2}$H$_{2}$F$_{4}$ and SF$_{6}$ are fluorinated greenhouse gases (F-gases) with Global Warming Potential (GWP) of $\approx$1400 and $\approx$22800, respectively. EU regulations imposed a progressive phase-down of C$_{2}$H$_{2}$F$_{4}$ production and consumption, aiming at strongly reducing its emission. This is already resulting in an increase of its price and reduction in availability. The most desirable long-term solution to this problem is to find an alternative, F-gases-free gas mixture, able to maintain similar detector performance. To address this challenge, the RPC ECOGasas@GIF++ collaboration (including RPC experts of ALICE, ATLAS, CMS, SHiP/LHCb, and the CERN EP-DT group) was created in 2019. The collaboration is currently studying a gas from the olefine family, the C$_{3}$H$_{2}$F$_{4}$ (or simply HFO, with GWP $\approx$6), to be used, in combination with CO$_{2}$, as a substitute for C$_{2}$H$_{2}$F$_{4}$. This contribution will focus on the signal shape studies that have been carried out by the collaboration during dedicated beam test periods. The methodology used in the data analysis will be presented, together with the results obtained with several HFO-based gas mixtures, and with the currently employed one. Furthermore, results on the counting-rate dependence of the RPC performance, obtained by combining the muon beam with the GIF++ $^{137}$Cs source with different attenuation factors, will also be presented.

ALICE (A Large Ion Collider Experiment) studies the Quark-Gluon Plasma (QGP): a deconfined state of matter obtained in ultra-relativistic heavy-ion collisions. One of the probes for QGP study are quarkonia and open heavy flavour, of which ALICE exploits the muonic decay. A set of Resistive Plate Chambers (RPCs), placed in the forward rapidity region of the ALICE detector, is used for muon identification purposes. The correct operation of these detectors is ensured by the choice of the proper gas mixture. Currently they are operated with a mixture of C$_{2}$H$_{2}$F$_{4}$, i-C$_{4}$H$_{10}$ and SF$_{6}$ but, starting from 2017, new EU regulations have enforced a progressive phase-out of C$_{2}$H$_{2}$F$_{4}$ because of its large Global Warming Potential (GWP), making it difficult and costly to purchase. CERN asked LHC experiments to reduce greenhouse gases emissions, to which RPC operation contributes significantly. A possible candidate for C$_{2}$H$_{2}$F$_{4}$ replacement is the C$_{3}$H$_{2}$F$_{4}$ (diluted with other gases, such as CO$_{2}$), which has been extensively tested using cosmic rays. Promising gas mixtures have been devised; the next crucial steps are the detailed in-beam characterization of such mixtures as well as the study of their performance under increasing irradiation levels. This contribution will describe the methodology and results of beam tests carried out at the CERN GIF++ (equipped with a high activity $^{137}$Cs source and muon beam) with an ALICE-like RPC prototype, operated with several mixtures with varying proportions of CO$_{2}$, C$_{3}$H$_{2}$F$_{4}$, i-C$_{4}$H$_{10}$ and SF$_{6}$ . Absorbed currents, efficiencies, prompt charges, cluster sizes, time resolutions and rate capabilities will be presented, both from digitized (for detailed shape and charge analysis) and discriminated (using the same front-end electronics as employed in ALICE) signals.

Resistive Plate Chambers (RPCs) are gaseous detectors widely used in high energy physics experiments, operating with a gas mixture primarily containing Tetrafluoroethane (C$_{2}$H$_{2}$F$_{4}$), commonly known as R-134a, which has a global warming potential (GWP) of 1430. To comply with European regulations and explore environmentally friendly alternatives, the RPC EcoGas@GIF++ collaboration, involving ALICE, ATLAS, CMS, LHCb/SHiP, and EP-DT communities, has undertaken intensive R\&D efforts to explore new gas mixtures for RPC technology. A leading alternative under investigation is HFO1234ze, boasting a low GWP of 6 and demonstrating reasonable performance compared to R-134a. Over the past few years, RPC detectors with slightly different characteristics and electronics have been studied using HFO and CO$_{2}$-based gas mixtures at the CERN Gamma Irradiation Facility. An aging test campaign was launched in August 2022, and during the latest test beam in July 2023, all detector systems underwent evaluation. This contribution will report the results of the aging studies and the performance evaluations of the detectors with and without irradiation.

Results obtained by the RPC ECOgas@GIF++ Collaboration, using Resistive Plate Chambers operated with new, eco-friendly gas mixtures, based on Tetrafluoropropene and carbon dioxide, are shown and discussed in this paper. Tests aimed to assess the performance of this kind of detectors in high-irradiation conditions, analogous to the ones foreseen for the coming years at the Large Hadron Collider experiments, were performed, and demonstrate a performance basically similar to the one obtained with the gas mixtures currently in use, based on Tetrafluoroethane, which is being progressively phased out for its possible contribution to the greenhouse effect. Long term aging tests are also being carried out, with the goal to demonstrate the possibility of using these eco-friendly gas mixtures during the whole High Luminosity phase of the Large Hadron Collider., Comment: Submitted to European Physical Journal C on October 24, 2023, 15 pages, 14 figures

The ALICE RPCs are operated with a mixture of 89.7% $C_{2}H_{2}F_{4}$, 10% i-$C_{4}H_{10}$ and 0.3% $SF_{6}$. $C_{2}H_{2}F_{4}$ and $SF_{6}$ are fluorinated greenhouse gases with a high Global Warming Potential (GWP). New European Union regulations have imposed a progressive phase-down of the production and usage of F-gases, aiming to cut down their emission by two thirds in 2030 with respect to 2014. Even though research activities are excluded from these regulations, the phase-down will inevitably increase their price and CERN is also aiming to cut down on its emissions. For these reasons it is crucial to find a more eco-friendly gas mixture for RPCs by the time of the LHC long shutdown 3, foreseen in 2026. Since $C_{2}H_{2}F_{4}$ is the main contributor to the mixture GWP, an extensive R&D process has started to replace it with tetrafluoropropene ($C_{3}H_{2}F_{4}$), due to its chemical similarity with $C_{2}H_{2}F_{4}$ and its low GWP (around 7). Preliminary tests with cosmic rays have shown promising results in terms of detector performance. The next step is to study the long-term behavior of RPCs operated with these new gas mixtures (aging studies). Since this is a subject of interest for all (and not only) the LHC experiments, a collaboration, ECOgas@GIF++, was setup to carry out joint studies. Among others, a small ALICE-like RPC was installed at the Gamma Irradiation Facility at CERN, where they are exposed to a strong radiation field, coming from a 12.5 TBq $^{137}$Cs source, which allows one to simulate many years of operation in a relatively short time. The facility also provides a muon beam at specific times of the year, which can be used to study the detector performance (e.g. efficiency and cluster size) during and after irradiation., Comment: Proceedings of the LHCp 2022 conference

The kicked top is one of the paradigmatic models in the study of quantum chaos~[F.~Haake et al., \emph{Quantum Signatures of Chaos (Springer Series in Synergetics vol 54)} (2018)]. Recently it has been shown that the onset of quantum chaos in the kicked top can be related to the proliferation of Trotter errors in digital quantum simulation (DQS) of collective spin systems. Specifically, the proliferation of Trotter errors becomes manifest in expectation values of few-body observables strongly deviating from the target dynamics above a critical Trotter step, where the spectral statistics of the Floquet operator of the kicked top can be predicted by random matrix theory. In this work, we study these phenomena in the framework of Hamiltonian learning (HL). We show how a recently developed Hamiltonian learning protocol can be employed to reconstruct the generator of the stroboscopic dynamics, i.e., the Floquet Hamiltonian, of the kicked top. We further show how the proliferation of Trotter errors is revealed by HL as the transition to a regime in which the dynamics cannot be approximately described by a low-order truncation of the Floquet-Magnus expansion. This opens up new experimental possibilities for the analysis of Trotter errors on the level of the generator of the implemented dynamics, that can be generalized to the DQS of quantum many-body systems in a scalable way. This paper is in memory of our colleague and friend Fritz Haake.

The wave-like nature of electrons leads to the existence of upper bounds on the thermoelectric response of nanostructured devices [R. S. Whitney, Phys. Rev. Lett. 112, 130601 (2014); Phys. Rev. B 91, 115425 (2015)]. This fundamental result, not present in classical thermodynamics, was demonstrated exploiting a two-terminal device modelled by non-linear scattering theory. In the present paper, we consider non-linear quantum transport through the same type of device working both as thermal machine and as refrigerator. For both operations, starting from charge and heat current expressions, we provide analytic quantum bounds for power exchanged, thermal currents and device efficiencies. For this purpose, we adopt a transmission function that maximizes the engine efficiency for given power output. For the optimal boxcar- or theta function-transmission shapes, we provide in a tutorial way an explicit deduction of the quantum bound expressions reported in the above cited papers., Comment: 16 pages, 3 figures, 2 tables

The goal of digital quantum simulation is to approximate the dynamics of a given target Hamiltonian via a sequence of quantum gates, a procedure known as Trotterization. The quality of this approximation can be controlled by the so called Trotter step, that governs the number of required quantum gates per unit simulation time. The stroboscopic dynamics generated by Trotterization is effectively described by a time-independent Hamiltonian, referred to as the Floquet Hamiltonian. In this work, we propose Floquet Hamiltonian learning to reconstruct the experimentally realized Floquet Hamiltonian order-by-order in the Trotter step. This procedure is efficient, i.e., it requires a number of measurements that scales polynomially in the system size, and can be readily implemented in state-of-the-art experiments. With numerical examples, we propose several applications of our method in the context of verification of quantum devices: from the characterization of the distinct sources of errors in digital quantum simulators to determining the optimal operating regime of the device. We show that our protocol provides the basis for feedback-loop design and calibration of new types of quantum gates. Furthermore it can be extended to the case of non-unitary dynamics and used to learn Floquet Liouvillians, thereby offering a way of characterizing the dissipative processes present in NISQ quantum devices.

This paper studies the performance of RPCs working with a new family of environment-friendly operating gases, mainly based on Carbon Dioxide and Hydro-Fluoro-Olefins. The tests are carried out on a 2 mm gap RPC and concern the measurement of detection efficiency, avalanche-to-streamer transition probability, prompt and ionic charge delivered. The timing properties of the new gas are also measured., Comment: 20 pages, 17 figures

Experimental studies of synthetic quantum matter are necessarily restricted to approximate ground states prepared on finite-size quantum simulators. In general, this limits their reliability for strongly correlated systems, for instance, in the vicinity of a quantum phase transition (QPT). Here, we propose a protocol that makes optimal use of a given finite-size simulator by directly preparing, on its bulk region, a mixed state representing the reduced density operator of the translation-invariant infinite-sized system of interest. This protocol is based on coherent evolution with a local deformation of the system Hamiltonian. For systems of free fermions in one and two spatial dimensions, we illustrate and explain the underlying physics, which consists of quasi-particle transport towards the system's boundaries while retaining the bulk "vacuum". For the example of a non-integrable extended Su-Schrieffer-Heeger model, we demonstrate that our protocol enables a more accurate study of QPTs. In addition, we demonstrate the protocol for an interacting spinful Fermi-Hubbard model with doping for 1D chains and a small two-leg ladder, where the initial state is a random superposition of energetically low-lying states.

The ATLAS RPC standard mixture, mainly based on C$_{2}$H$_{2}$F$_{4}$, has a high Global Warming Potential (GWP) and therefore the search for RPC eco friendly gases is mandatory. In this work we present the results on the detector performances in terms of efficiency, prompt and ionic charge, with different gas mixtures.

The intrinsic rate capability and the ageing properties of the Resistive Plate Chambers are closely related to the electrodes material and to the front-end electronics threshold. The development of a low noise pre-amplifier led us to improve the intrinsic rate capability of High Pressure Laminate (bakelite) up to $\sim10\;kHz/cm^2$, nevertheless the effective rate is significantly limited by electrodes ageing. To further improve the effective rate capability new materials are investigated. A Resistive Plate Chamber with crystalline semi-insulating Gallium Arsenide electrodes has been characterized with high energy electrons beam at the Beam Test Facility (BFT), (INFN National Laboratory of Frascati, Italy). The response of the Resistive Plate Chamber to multiple bunched electrons was measured operating the detector in avalanche mode. The intrinsic rate capability has been also measured operating the detector in a uniform high energy gamma radiation field at the GIF++ facility (EHN1 of SPS, CERN)., Comment: 6 pages, 7 figures

Motivated by recent experimental progress in the study of quantum systems far from equilibrium, we investigate the relation between several dynamical signatures of topology in the coherent time-evolution after a quantum quench. Specifically, we study the conditions for the appearance of entanglement spectrum crossings, dynamical quantum phase transitions, and dynamical Chern numbers. For non-interacting models, we show that in general there is no direct relation between these three quantities. Instead, we relate the presence of level crossings in the entanglement spectrum to localized boundary modes that may not be of topological origin in the conventional sense. Finally, we investigate how interactions influence the presence of entanglement spectrum crossings and dynamical quantum phase transitions, by means of time-dependent density matrix renormalization group simulations., Comment: Updated version

Quantum entanglement may have various origins ranging from solely interaction-driven quantum correlations to single-particle effects. Here, we explore the dependence of entanglement on time-dependent single-particle basis transformations in fermionic quantum many-body systems, thus aiming at isolating single-particle sources of entanglement growth in quench dynamics. Using exact diagonalization methods, for paradigmatic non-integrable models we compare to the standard real space cut various physically motivated bipartitions. Moreover, we search for a minimal entanglement basis using local optimization algorithms, which at short to intermediate post-quench times yields a significant reduction of entanglement beyond a dynamical Hartree-Fock solution. In the long-time limit, we identify an asymptotic universality of entanglement for weakly interacting systems, as well as a cross-over from dominant real-space to momentum-space entanglement in Hubbard-models undergoing an interaction quench. Finally, we discuss the relevance of our findings for the development of tensor network based algorithms for quantum dynamics., Comment: Updated version with minor modifications