Quantum machine learning of graph-structured data

Graph structures are ubiquitous throughout the natural sciences. Here we develop an approach that exploits the quantum source's graph structure to improve learning via an arbitrary quantum neural network (QNN) ansatz. In particular, we devise and optimize a self-supervised objective to capture the information-theoretic closeness of the quantum states in the training of a QNN. Numerical simulations show that our approach improves the learning efficiency and the generalization behavior of the base QNN. On a practical note, scalable quantum implementations of the learning procedure described in this paper are likely feasible on the next generation of quantum computing devices.Multimedia ComputingIndustrial Design Engineerin

Transparent silicon carbide/tunnel SiO₂ passivation for c-Si solar cell front side: Enabling J_sc > 42 mA/cm² and iV_oc of 742 mV

N-type microcrystalline silicon carbide (μc-SiC:H(n)) is a wide bandgap material that is very promising for the use on the front side of crystalline silicon (c-Si) solar cells. It offers a high optical transparency and a suitable refractive index that reduces parasitic absorption and reflection losses, respectively. In this work, we investigate the potential of hot wire chemical vapor deposition (HWCVD)–grown μc-SiC:H(n) for c-Si solar cells with interdigitated back contacts (IBC). We demonstrate outstanding passivation quality of μc-SiC:H(n) on tunnel oxide (SiO2)–passivated c-Si with an implied open-circuit voltage of 742 mV and a saturation current density of 3.6 fA/cm2. This excellent passivation quality is achieved directly after the HWCVD deposition of μc-SiC:H(n) at 250°C heater temperature without any further treatments like recrystallization or hydrogenation. Additionally, we developed magnesium fluoride (MgF2)/silicon nitride (SiNx:H)/silicon carbide antireflection coatings that reduce optical losses on the front side to only 0.47 mA/cm2 with MgF2/SiNx:H/μc-SiC:H(n) and 0.62 mA/cm2 with MgF2/μc-SiC:H(n). Finally, calculations with Sentaurus TCAD simulation using MgF2/μc-SiC:H(n)/SiO2/c-Si as front side layer stack in an IBC solar cell reveal a short-circuit current density of 42.2 mA/cm2, an open-circuit voltage of 738 mV, a fill factor of 85.2% and a maximum power conversion efficiency of 26.6%.Photovoltaic Materials and DevicesElectrical Sustainable Energ

CGILS: Results from the first phase of an international project to understand the physical mechanisms of low cloud feedbacks in single column models

CGILS—the CFMIP-GASS Intercomparison of Large Eddy Models (LESs) and single column models (SCMs)—investigates the mechanisms of cloud feedback in SCMs and LESs under idealized climate change perturbation. This paper describes the CGILS results from 15 SCMs and 8 LES models. Three cloud regimes over the subtropical oceans are studied: shallow cumulus, cumulus under stratocumulus, and well-mixed coastal stratus/stratocumulus. In the stratocumulus and coastal stratus regimes, SCMs without activated shallow convection generally simulated negative cloud feedbacks, while models with active shallow convection generally simulated positive cloud feedbacks. In the shallow cumulus alone regime, this relationship is less clear, likely due to the changes in cloud depth, lateral mixing, and precipitation or a combination of them. The majority of LES models simulated negative cloud feedback in the well-mixed coastal stratus/stratocumulus regime, and positive feedback in the shallow cumulus and stratocumulus regime. A general framework is provided to interpret SCM results: in a warmer climate, the moistening rate of the cloudy layer associated with the surface-based turbulence parameterization is enhanced; together with weaker large-scale subsidence, it causes negative cloud feedback. In contrast, in the warmer climate, the drying rate associated with the shallow convection scheme is enhanced. This causes positive cloud feedback. These mechanisms are summarized as the “NESTS” negative cloud feedback and the “SCOPE” positive cloud feedback (Negative feedback from Surface Turbulence under weaker Subsidence—Shallow Convection PositivE feedback) with the net cloud feedback depending on how the two opposing effects counteract each other. The LES results are consistent with these interpretations.Geoscience & Remote SensingCivil Engineering and Geoscience