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In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits and proof-of-principle error-correction on a single logical qubit. Nevertheless, despite significant progress and excitement, the path toward a full-stack scalable technology is largely unknown. There are significant outstanding quantum hardware, fabrication, software architecture, and algorithmic challenges that are either unresolved or overlooked. These issues could seriously undermine the arrival of utility-scale quantum computers for the foreseeable future. Here, we provide a comprehensive review of these scaling challenges. We show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits, employing system engineering approaches, and performing distributed quantum computation within heterogeneous high-performance computing infrastructures. These opportunities for research and development could unlock certain promising applications, in particular, efficient quantum simulation/learning of quantum data generated by natural or engineered quantum systems. To estimate the true cost of such promises, we provide a detailed resource and sensitivity analysis for classically hard quantum chemistry calculations on surface-code error-corrected quantum computers given current, target, and desired hardware specifications based on superconducting qubits, accounting for a realistic distribution of errors. Furthermore, we argue that, to tackle industry-scale classical optimization and machine learning problems in a cost-effective manner, distributed quantum-assisted probabilistic computing with custom-designed accelerators should be considered as a complementary path toward scalability., Comment: 50 pages, 42 figures

The increasing availability of GPUs for scientific computing has prompted interest in accelerating quantum chemical calculations through their use. The complexity of integral kernels for high angular momentum basis functions however often limits the utility of GPU implementations with large basis sets or for metal containing systems. In this work, we report implementation of $f$ function support in the GPU-accelerated TeraChem software package through the development of efficient kernels for the evaluation of Hamiltonian integrals. The high efficiency of the resulting code is demonstrated through density functional theory (DFT) calculations on increasingly large organic molecules and transition metal complexes, as well as coupled cluster singles and doubles (CCSD) calculations on water clusters. Preliminary investigations into Ni(I) catalysis with DFT and the photochemistry of MnH(CH$_3$) with complete active space self-consistent field (CASSCF) are also carried out. Overall, our GPU-accelerated software appears to be well-suited for fast simulation of large transition metal containing systems, as well as organic molecules., Comment: Added additional data

We study the young star cluster populations in 23 dwarf and irregular galaxies observed by the HST Legacy ExtraGalactic Ultraviolet Survey (LEGUS), and examine relationships between the ensemble properties of the cluster populations and those of their host galaxies: star formation rate (SFR) density ($\Sigma_{\rm SFR}$). A strength of this analysis is the availability of SFRs measured from temporally resolved star formation histories which provide the means to match cluster and host-galaxy properties on several timescales (1-10, 1-100, and 10-100~Myr). Nevertheless, studies of this kind are challenging for dwarf galaxies due to the small numbers of clusters in each system. We mitigate these issues by combining the clusters across different galaxies with similar $\Sigma_{\rm SFR}$ properties. We find good agreement with a well-established relationship ($M_{V}^{brightest}$-SFR), but find no significant correlations between $\Sigma_{\rm SFR}$ and the slopes of the cluster luminosity function, mass function, nor the age distribution. We also find no significant trend between the the fraction of stars in bound clusters at different age ranges ($\Gamma_{1-10}$, $\Gamma_{10-100}$, and $\Gamma_{1-100}$) and $\Sigma_{\rm SFR}$ of the host galaxy. Our data show a decrease in $\Gamma$ over time (from 1-10 to 10-100~Myr) suggesting early cluster dissolution, though the presence of unbound clusters in the youngest time bin makes it difficult to quantify the degree of dissolution. While our data do not exhibit strong correlations between $\Sigma_{\rm SFR}$ and ensemble cluster properties, we cannot rule out that a weak trend might exist given the relatively large uncertainties due to low number statistics and the limited $\Sigma_{\rm SFR}$ range probed., Comment: 30 pages, 15 figures, 7 tables, Accepted for publication in MNRAS

The ExoClock project has been created with the aim of increasing the efficiency of the Ariel mission. It will achieve this by continuously monitoring and updating the ephemerides of Ariel candidates over an extended period, in order to produce a consistent catalogue of reliable and precise ephemerides. This work presents a homogenous catalogue of updated ephemerides for 450 planets, generated by the integration of $\sim$18000 data points from multiple sources. These sources include observations from ground-based telescopes (ExoClock network and ETD), mid-time values from the literature and light-curves from space telescopes (Kepler/K2 and TESS). With all the above, we manage to collect observations for half of the post-discovery years (median), with data that have a median uncertainty less than one minute. In comparison with literature, the ephemerides generated by the project are more precise and less biased. More than 40\% of the initial literature ephemerides had to be updated to reach the goals of the project, as they were either of low precision or drifting. Moreover, the integrated approach of the project enables both the monitoring of the majority of the Ariel candidates (95\%), and also the identification of missing data. The dedicated ExoClock network effectively supports this task by contributing additional observations when a gap in the data is identified. These results highlight the need for continuous monitoring to increase the observing coverage of the candidate planets. Finally, the extended observing coverage of planets allows us to detect trends (TTVs - Transit Timing Variations) for a sample of 19 planets. All products, data, and codes used in this work are open and accessible to the wider scientific community., Comment: Recommended for publication to ApJS (reviewer's comments implemented). Main body: 13 pages, total: 77 pages, 7 figures, 7 tables. Data available at http://doi.org/10.17605/OSF.IO/P298N

We propose a neural network framework to preclude the need to define or observe incompletely or inaccurately defined states of a material in order to describe its response. The neural network design is based on the classical Coleman-Gurtin internal state variable theory. In the proposed framework the states of the material are inferred from observable deformation and stress. A neural network describes the flow of internal states and another represents the map from internal state and strain to stress. We investigate tensor basis, component, and potential-based formulations of the stress model. Violations of the second law of thermodynamics are prevented by a constraint on the weights of the neural network. We extend this framework to homogenization of materials with microstructure with a graph-based convolutional neural network that preprocesses the initial microstructure into salient features. The modeling framework is tested on large datasets spanning inelastic material classes to demonstrate its general applicability., Comment: 46 pages, 16 figures