Authors: George Rajna
The race to build larger and larger quantum computers is heating up, with several technologies competing for a role in future devices. Each potential platform has strengths and weaknesses, but little has been done to directly compare the performance of early prototypes. Now, researchers at the JQI have performed a first-of-its-kind benchmark test of two small quantum computers built from different technologies.  To find out whether quantum computers will work properly, scientists must simulate them on a classical computer. Now a record-breaking experiment has simulated the largest quantum computer yet.  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: 31 Pages.
[v1] 2017-04-13 11:08:43
Unique-IP document downloads: 8 times
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.