Authors: George Rajna
Two teams of researchers in China have advanced the distance that entangled particles can be used to send information, including encryption keys.  Scientists at the University of York's Centre for Quantum Technology have made an important breakthrough in the theory of quantum secure communications.  How to reliably transfer quantum information when the connecting channels are impacted by detrimental noise? Scientists at the University of Innsbruck and TU Wien (Vienna) have presented new solutions to this problem.  Adding to strong recent demonstrations that particles of light perform what Einstein called "spooky action at a distance," in which two separated objects can have a connection that exceeds everyday experience, physicists at the National Institute of Standards and Technology (NIST) have confirmed that particles of matter can act really spooky too.  How fast will a quantum computer be able to calculate? While fully functional versions of these long-sought technological marvels have yet to be built, one theorist at the National Institute of Standards and Technology (NIST) has shown that, if they can be realized, there may be fewer limits to their speed than previously put forth.  Unlike experimental neuroscientists who deal with real-life neurons, computational neuroscientists use model simulations to investigate how the brain functions.  A pair of physicists with ETH Zurich has developed a way to use an artificial neural network to characterize the wave function of a quantum many-body system.  A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website.  Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics.  A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. 
Comments: 33 Pages.
[v1] 2017-07-11 07:52:28
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