4 Works

Quantum supremacy using a programmable superconducting processor

John M. Martinis, Sergio Boixo, Hartmut Neven, Frank Arute, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph C. Bardin, Rami Barends, Rupak Biswas, Fernando G. S. L. Brandao, David A. Buell, Brian Burkett, Yu Chen, Zijun Chen, Ben Chiaro, Roberto Collins, William Courtney, Andrew Dunsworth, Edward Farhi, Brooks Foxen, Austin Fowler, Craig Gidney, Marissa Giustina, Rob Graff … & Adam Zalcman
The tantalizing promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor. A fundamental challenge is to build a high-fidelity processor capable of running quantum algorithms in an exponentially large computational space. Here, we report using a processor with programmable superconducting qubits to create quantum states on 53 qubits, corresponding to a computational state-space of dimension 2^53 ∼ 10^16. Measurements from repeated...

Data from: Recent decadal drought reverts warming–triggered growth enhancement in contrasting climates in the southern Andes treeline

Alex Fajardo, Antonio Gazol, Christoph Mayr & J. Julio Camarero
Aims: Rising temperature and declining summer precipitation due to the 1970s-climate shift in southern South America have reduced forest productivity at dry sites. Here, we worked with the most widespread Southern Hemisphere treeline species, Nothofagus pumilio, across contrasting climatic conditions and determined whether rising atmospheric CO2 concentrations as well as warmer and drier climatic conditions provoked by the 70s-climatic shift have been causing systematic changes in treeline growth rates and intrinsic water-use efficiency (iWUE).Location: 36–54°...

Data from: Modeling spatiotemporal abundance of mobile wildlife in highly variable environments using boosted GAMLSS hurdle models

Adam Smith, Benjamin Hofner, Juliet S. Lamb, Jason Osenkowski, Taber Allison, Giancarlo Sadoti, Scott McWilliams & Peter Paton
1. Modeling organism distributions from survey data involves numerous statistical challenges, including zero-inflation, overdispersion, and selection and incorporation of environmental covariates. In environments with high spatial and temporal variability, addressing these challenges often requires numerous assumptions regarding organism distributions and their relationships to biophysical features. These assumptions may limit the resolution or accuracy of predictions resulting from survey-based distribution models. 2. We propose an iterative modeling approach that incorporates a negative binomial hurdle, followed by...

Data from: Objective estimation of sensory thresholds based on neurophysiological parameters

Achim Schilling, Richard Gerum, Patrick Krauss, Claus P. D. Metzner, Konstantin Tziridis & Holger Schulze
Reliable determination of sensory thresholds is the holy grail of signal detection theory. However, there exists no assumption-independent gold standard for the estimation of thresholds based on neurophysiological parameters, although a reliable estimation method is crucial for both scientific investigations and clinical diagnosis. Whenever it is impossible to communicate with the subjects, as in studies with animals or neonates, thresholds have to be derived from neural recordings or by indirect behavioral tests. Whenever the threshold...

Registration Year

  • 2019

Resource Types

  • Dataset


  • University of Erlangen-Nuremberg
  • University of Massachusetts Amherst
  • University of Michigan–Ann Arbor
  • Google (United States)
  • University of Rhode Island
  • Rhode Island Department of Environmental Management
  • University of Nevada Reno
  • Oak Ridge National Laboratory
  • Stinger Ghaffarian Technologies (United States)
  • Ames Research Center