Authors: George Rajna
Quantum image processing (QIP) is an emerging sub-discipline that is focused on extending conventional image processing tasks and operations to the quantum computing framework.  The team's experimental collaborators have already demonstrated the technology, yielding cluster states composed of more than 1 million entangled modes.  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. 
Comments: 33 Pages.
[v1] 2017-04-11 11:16:41
Unique-IP document downloads: 44 times
Vixra.org is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. Vixra.org will not be responsible for any consequences of actions that result from any form of use of any documents on this website.
Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.