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We present an industrial end-user perspective on the current state of quantum computing hardware for one specific technological approach, the neutral atom platform. Our aim is to assist developers in understanding the impact of the specific properties of these devices on the effectiveness of algorithm execution. Based on discussions with different vendors and recent literature, we discuss the performance data of the neutral atom platform. Specifically, we focus on the physical qubit architecture, which affects state preparation, qubit-to-qubit connectivity, gate fidelities, native gate instruction set, and individual qubit stability. These factors determine both the quantum-part execution time and the end-to-end wall clock time relevant for end-users, but also the ability to perform fault-tolerant quantum computation in the future. We end with an overview of which applications have been shown to be well suited for the peculiar properties of neutral atom-based quantum computers.

We present an industrial end-user perspective on the current state of quantum computing hardware for one specific technological approach, the neutral atom platform. Our aim is to assist developers in understanding the impact of the specific properties of these devices on the effectiveness of algorithm execution. Based on discussions with different vendors and recent literature, we discuss the performance data of the neutral atom platform. Specifically, we focus on the physical qubit architecture, which affects state preparation, qubit-to-qubit connectivity, gate fidelities, native gate instruction set, and individual qubit stability. These factors determine both the quantum-part execution time and the end-to-end wall clock time relevant for end-users, but also the ability to perform fault-tolerant quantum computation in the future. We end with an overview of which applications have been shown to be well suited for the peculiar properties of neutral atom-based quantum computers.

This is the second paper in a series of papers providing an overview of different quantum computing hardware platforms from an industrial end-user perspective. It follows our first paper on neutral-atom quantum computing. In the present paper, we provide a survey on the current state-of-the-art in trapped-ion quantum computing, taking up again the perspective of an industrial end-user. To this end, our paper covers, on the one hand, a comprehensive introduction to the physical foundations and mechanisms that play an important role in operating a trapped-ion quantum computer. On the other hand, we provide an overview of the key performance metrics that best describe and characterise such a device's current computing capability. These metrics encompass performance indicators such as qubit numbers, gate times and errors, native gate sets, qubit stability and scalability as well as considerations regarding the general qubit types and trap architectures. In order to ensure that these metrics reflect the current state of trapped-ion quantum computing as accurate as possible, they have been obtained by both an extensive review of recent literature and, more importantly, from discussions with various quantum hardware vendors in the field. We combine these factors and provide - again from an industrial end-user perspective - an overview of what is currently possible with trapped-ion quantum computers, which algorithms and problems are especially suitable for this platform, what are the relevant end-to-end wall clock times for calculations, and what might be possible with future fault-tolerant trapped-ion quantum computers.

Disturbances from rodent engineering and human activities profoundly impact ecosystem structure and functioning. Whilst we know that disturbances modulate plant communities, comprehending the mechanisms through which rodent and human disturbances influence the functional trait diversity and trait composition of plant communities is important to allow projecting future changes and to enable informed decisions in response to changing intensity of the disturbances. Here, we evaluated the changes in functional trait diversity and composition of Afroalpine plant communities in the Bale Mountains of Ethiopia along gradients of engineering disturbances of a subterranean endemic rodent, the giant root-rat (Tachyoryctes macrocephalus Rüppell 1842) and human activities (settlement establishment and livestock grazing). We conducted RLQ (co-inertia analysis) and fourth-corner analyses to test for trait-disturbance (rodent engineering/human activities) covariation. Overall, our results show an increase in plant functional trait diversity with increasing root-rat engineering and increasing human activities. We found disturbance specific association with traits. Specifically, we found strong positive association of larger seed mass with increasing root-rat fresh burrow density, rhizomatous vegetative propagation negatively associated with increasing root-rat old burrow, and stolonifereous vegetative propagation positively associated with presence of root-rat mima mound. Moreover, both leaf size and leaf nitrogen content were positively associated with livestock dung abundance but negatively with distance from settlement. Overall, our results suggest that disturbances by rodents filter plant traits related to survival and reproduction strategies, whereas human activities such as livestock grazing act as filters for traits related to leaf economics spectrum along acquisitive resource-use strategy.

Subterranean animals act as ecosystem engineers, for example, through soil perturbation and herbivory, shaping their environments worldwide. As the occurrence of animals is often linked to above‐ground features such as plant species composition or landscape textures, satellite‐based remote sensing approaches can be used to predict the distribution of subterranean species. Here, we combine in‐situ collected vegetation composition data with remotely sensed data to improve the prediction of a subterranean species across a large spatial scale. We compared three machine learning‐based modeling strategies, including field and satellite‐based remote sensing data to different extents, in order to predict the distribution of the subterranean giant root‐rat GRR, Tachyoryctes macrocephalus, an endangered rodent species endemic to the Bale Mountains in southeast Ethiopia. We included no, some and extensive fieldwork data in the modeling to test how these data improved prediction quality. We found prediction quality to be particularly dependent on the spatial coverage of the training data. Species distributions were best predicted by using texture metrics and eyeball‐selected data points of landscape marks created by the GRR. Vegetation composition as a predictor showed the lowest contribution to model performance and lacked spatial accuracy. Our results suggest that the time‐consuming collection of vegetation data in the field is not necessarily required for the prediction of subterranean species that leave traceable above‐ground landscape marks like the GRR. Instead, remotely sensed and spatially eyeball‐selected presence data of subterranean species could profoundly enhance predictions. The usage of remote sensing‐derived texture metrics has great potential for improving the distribution modeling of subterranean species, especially in arid ecosystems. We compared three machine learning‐based modeling strategies, which included field‐ and remote‐sensing data to a different extent, for predicting the distribution of a subterranean species. We used the endangered giant root‐rat Tachyoryctes macrocephalus, endemic to the afro‐alpine ecosystem of the Bale Mountains in Ethiopia, for demonstrating that remotely sensed and spatially eyeball‐selected presence data of subterranean species could profoundly enhance distribution predictions. Our results suggest that the time‐consuming collection of vegetation data in the field is not necessarily required for the distribution prediction of subterranean species that leave traceable above‐ground marks in the landscape.

Human activities, directly and indirectly, impact ecological engineering activities of subterranean rodents. As engineering activities of burrowing rodents are affected by, and reciprocally affect vegetation cover via feeding, burrowing and mound building, human influence such as settlements and livestock grazing, could have cascading effects on biodiversity and ecosystem processes such as bioturbation. However, there is limited understanding of the relationship between human activities and burrowing rodents. The aim of this study was therefore to understand how human activities influence the ecological engineering activity of the giant root‐rat (Tachyoryctes macrocephalus), a subterranean rodent species endemic to the Afroalpine ecosystem of the Bale Mountains of Ethiopia. We collected data on human impact, burrowing activity and vegetation during February and March of 2021. Using path analysis, we tested (1) direct effects of human settlement on the patterns of livestock grazing intensity, (2) direct and indirect impacts of humans and livestock grazing intensity on the root‐rat burrow density and (3) whether human settlement and livestock grazing influence the effects of giant root‐rat burrow density on vegetation and vice versa. We found lower levels of livestock grazing intensity further from human settlement than in its proximity. We also found a significantly increased giant root‐rat burrow density with increasing livestock grazing intensity. Seasonal settlement and livestock grazing intensity had an indirect negative and positive effect on giant root‐rat burrow density, respectively, both via vegetation cover. Analysing the reciprocal effects of giant root‐rat on vegetation, we found a significantly decreased vegetation cover with increasing density of giant root‐rat burrows, and indirectly with increasing livestock grazing intensity via giant root‐rat burrow density. Our results demonstrate that giant root‐rats play a synanthropic engineering role that affects vegetation structure and ecosystem processes. In our study, we tested how human activities in terms of livestock grazing and settlements affect the reciprocal effects between vegetation and giant root‐rats. Our results show that increasing livestock grazing intensity causes increases in giant root‐rat burrow density indirectly through decreased vegetation cover. Reciprocally, the root‐rat's engineering activities resulted in decreased vegetation cover.

Due to their limited dispersal ability, fossorial species with predominantly belowground activity usually show increased levels of population subdivision across relatively small spatial scales. This may be exacerbated in harsh mountain ecosystems, where landscape geomorphology limits species’ dispersal ability and leads to small effective population sizes, making species susceptible to environmental change. The giant root-rat (Tachyoryctes macrocephalus) is a highly fossorial rodent confined to the afro-alpine ecosystem of the Bale Mountains in Ethiopia. Using mitochondrial and low-coverage nuclear genomes, we investigated 77 giant root-rat individuals sampled from nine localities across its whole ∼1,000 km2 range. Our data revealed a distinct division into a northern and southern subpopulation, with no signs of gene flow, and higher nuclear genetic diversity in the south. Landscape genetic analyses of the mitochondrial genomes indicated that population subdivision was driven by steep slopes and elevation differences of up to 500 m across escarpments separating the north and south, potentially reinforced by glaciation of the south during the Late Pleistocene (∼42,000 to 16,000 years ago). Despite the pronounced subdivision observed at the range-wide scale, weak geographic structuring of sampling localities within subpopulations indicated gene flow across distances of at least 16 km, suggesting aboveground dispersal and high mobility for relatively long distances. Our study highlights how topographic barriers can lead to the genetic subdivision of fossorial species, despite their potential to maintain gene flow at the local scale. These factors can reduce genetic variability, which should be considered when developing conservation strategies.