Quantum computing with defects

Abstract, The successful development of quantum computers is dependent on identifying quantum systems to function as qubits. Paramagnetic states of point defects in semiconductors or insulators have been shown to provide an effective implementation, with the nitrogen-vacancy center in diamond being a prominent example. The spin-1 ground state of this center can be initialized, manipulated, and read out at room temperature. Identifying defects with similar properties in other materials would add flexibility in device design and possibly lead to superior performance or greater functionality. A systematic search for defect-based qubits has been initiated, starting from a list of physical criteria that such centers and their hosts should satisfy. First-principles calculations of atomic and electronic structure are essential in supporting this quest: They provide a deeper understanding of defects that are already being exploited and allow efficient exploration of new materials systems and "defects by design.

Origin and passivation of fixed charge in atomic layer deposited aluminum oxide gate insulators on chemically treated InGaAs substrates

We report experimental and theoretical studies of defects producing fixed charge within Al(2)O(3) layers grown by atomic layer deposition (ALD) on In(0.53)Ga(0.47)As(001) substrates and the effects of hydrogen passivation of these defects. Capacitance-voltage measurements of Pt/ALD-Al(2)O(3)/n-In(0.53)Ga(0.47)As suggested the presence of positive bulk fixed charge and negative interfacial fixed charge within ALD-Al(2)O(3). We identified oxygen and aluminum dangling bonds (DBs) as the origin of the fixed charge. First-principles calculations predicted possible passivation of both O and Al DBs, which would neutralize fixed charge, and this prediction was confirmed experimentally; postmetallization forming gas anneal removed most of the fixed charge in ALD-Al(2)O(3). (C) 2010 American Institute of Physics. (doi:10.1063/1.3399776

Modeling the Effect of Material Properties on Liquid-Alkaline Water Electrolysis

Liquid-alkaline water electrolyzers (LAWEs) use electricity to drive the conversion of water to H2 and O2 gas. These devices benefit from the use of low-cost nickel electrodes and metal-oxide separators, but suffer from lower current densities and higher cell voltages than proton-exchange-membrane water electrolyzers. Identifying the inefficiencies that result in this poor performance is key to mitigating losses and optimizing LAWEs. Here, we report an experimentally-validated 1-D continuum model of a LAWE that elucidates the gradients within the cell, simulates H2 crossover, and projects the energy improvements made possible by modulating the properties of the electrodes and separator. The model captures the Nernstian polarization losses and the distribution of gas- and liquid-phases within the electrodes, enabling quantification of energy losses associated with kinetic, ohmic, and bubble-induced (mass-transport) resistances. Simulations demonstrate that LAWE can achieve energy intensities of 50 kWh kg−1 of H2 at 1 A cm−2 using improved electrode and separator properties

Determining the Electronic Confinement of a Subsurface Metallic State

Dopant profiles in semiconductors are important for understanding nanoscale electronics. Highly conductive and extremely confined phosphorus doping profiles in silicon, known as Si:P δ-layers, are of particular interest for quantum computer applications, yet a quantitative measure of their electronic profile has been lacking. Using resonantly enhanced photoemission spectroscopy, we reveal the real-space breadth of the Si:P δ-layer occupied states and gain a rare view into the nature of the confined orbitals. We find that the occupied valley-split states of the δ-layer, the so-called 1Γ and 2Γ, are exceptionally confined with an electronic profile of a mere 0.40 to 0.52 nm at full width at half-maximum, a result that is in excellent agreement with density functional theory calculations. Furthermore, the bulk-like Si 3pz orbital from which the occupied states are derived is sufficiently confined to lose most of its pz-like character, explaining the strikingly large valley splitting observed for the 1Γ and 2Γ states

The Long-Baseline Neutrino Experiment: Exploring Fundamental Symmetries of the Universe

The preponderance of matter over antimatter in the early Universe, the dynamics of the supernova bursts that produced the heavy elements necessary for life and whether protons eventually decay --- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our Universe, its current state and its eventual fate. The Long-Baseline Neutrino Experiment (LBNE) represents an extensively developed plan for a world-class experiment dedicated to addressing these questions. LBNE is conceived around three central components: (1) a new, high-intensity neutrino source generated from a megawatt-class proton accelerator at Fermi National Accelerator Laboratory, (2) a near neutrino detector just downstream of the source, and (3) a massive liquid argon time-projection chamber deployed as a far detector deep underground at the Sanford Underground Research Facility. This facility, located at the site of the former Homestake Mine in Lead, South Dakota, is approximately 1,300 km from the neutrino source at Fermilab -- a distance (baseline) that delivers optimal sensitivity to neutrino charge-parity symmetry violation and mass ordering effects. This ambitious yet cost-effective design incorporates scalability and flexibility and can accommodate a variety of upgrades and contributions. With its exceptional combination of experimental configuration, technical capabilities, and potential for transformative discoveries, LBNE promises to be a vital facility for the field of particle physics worldwide, providing physicists from around the globe with opportunities to collaborate in a twenty to thirty year program of exciting science. In this document we provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess.Comment: Major update of previous version. This is the reference document for LBNE science program and current status. Chapters 1, 3, and 9 provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess. 288 pages, 116 figure

Relationship between foramen magnum position and locomotion in extant and extinct hominoids

International audienceFrom the Miocene Sahelanthropus tchadensis to Pleistocene Homo sapiens, hominins are characterized by a derived anterior position of the foramen magnum relative to basicranial structures. It has been previously suggested that the anterior position of the foramen magnum in hominins is related to bipedal locomotor behavior. Yet, the functional relationship between foramen magnum position and bipedal locomotion remains unclear. Recent studies, using ratios based on cranial linear measurements, have found a link between the anterior position of the foramen magnum and bipedalism in several mammalian clades: marsupials, rodents, and primates. In the present study, we compute these ratios in a sample including a more comprehensive dataset of extant hominoids and fossil hominins. First, we verify if the values of ratios can distinguish extant humans from apes. Then, we test whether extinct hominins can be distinguished from non-bipedal extant hominoids. Finally, we assess if the studied ratios are effective predictors of bipedal behavior by testing if they mainly relate to variation in foramen magnum position rather than changes in other cranial structures. Our results confirm that the ratios discriminate between extant bipeds and non-bipeds. However, the only ratio clearly discriminating between fossil hominins and other extant apes is that which only includes basicranial structures. We show that a large proportion of the interspecific variation in the other ratios relates to changes in facial, rather than basicranial, structures. In this context, we advocate the use of measurements based only on basicranial structures when assessing the relationship between foramen magnum position and bipedalism in future studies

Modern American populism: Analyzing the economics behind the Silent Majority, the Tea Party and Trumpism

This article researches populism, more specifically, Modern American Populism (MAP), constructed of white, rural, and economically oppressed reactionarianism, which was borne out of the political upheaval of the 1960’s Civil Rights movement. The research looks to explain the causes of populism and what leads voters to support populist movements and politicians. The research focuses on economic anxiety as the main cause but also examines an alternative theory of racial resentment. In an effort to answer the question, what causes populist movements and motivations, I apply a research approach that utilizes qualitative and quantitative methods. There is an examination of literature that defines populism, its causes and a detailed discussion of the case studies, including the 1972 election of Richard Nixon; the Tea Party election of 2010; and the 2016 election of Donald Trump. In addition, statistical data analysis was run using American National Election Studies (ANES) surveys associated with each specific case study. These case studies were chosen because they most represent forms of populist movements in modern American history. While ample qualitative evidence suggested support for the hypothesis that economic anxiety is a necessary condition for populist voting patterns that elected Nixon, the Tea Party and Trump, the statistical data only supported the hypothesis in two cases, 2010 and 2016, with 1972 coming back inconclusive. The data also suggested that both economic anxiety and racial resentment played a role in 2010 and 2016, while having no significant effect in 1972 in either case. This suggests that further research needs to be conducted into additional populist case studies, as well as an examination into the role economic anxiety and economic crises play on racial resentment and racially motivated voting behavior

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19