Lifeboat Foundation

Researchers have created a new testing ground for quantum systems in which they can literally turn certain particle interactions on and off, potentially paving the way for advances in spintronics.

Spin transport electronics have the potential to revolutionize electronic devices as we know them, especially when it comes to computing. While standard electronics use an electron’s charge to encode information, spintronic devices rely on another intrinsic property of the electron: its spin.

Spintronics could be faster and more reliable than conventional electronics, as spin can be changed quickly and these devices use less power. However, the field is young and there are many questions researchers need to solve to improve their control of spin information. One of the most complex questions plaguing the field is how the signal carried by particles with spin, known as spin current, decays over time.

The entangled photons were beamed to three ground stations across China, each separated by more than 700 miles—a new record.

A participation in the annual Supercomputing conference taking place in Salt Lake City, UT, USA from November 14–17, 2016.

Buckyballs! We love them.

JILA researchers have measured hundreds of individual quantum energy levels in the buckyball, a spherical cage of 60 carbon atoms. It’s the largest molecule that has ever been analyzed at this level of experimental detail in the history of quantum mechanics. Fully understanding and controlling this molecule’s quantum details could lead to new scientific fields and applications, such as an entire quantum computer contained in a single buckyball.

Engineering researchers have demonstrated proof-of-principle for a device that could serve as the backbone of a future quantum Internet. University of Toronto Engineering professor Hoi-Kwong Lo and his collaborators have developed a prototype for a key element for all-photonic quantum repeaters, a critical step in long-distance quantum communication.

One of the most powerful techniques in machine learning, generative adversarial networks, has been tested on a quantum computer for the first time.

Quantum physics just beat classical physics again.

A single quantum particle can send a two-way signal, scientists have discovered — something that’s impossible in classical physics. That means a particle can essentially send messages to itself thanks to the whacky state of uncertainty known as superposition.

Superposition states that one particle can occupy two positions at once, and that’s how the two-way communication happens.

Continue reading “Quantum Theory Bends The Limits of Physics, Showing Two-Way Signaling May Be Possible” »

In May, 2016 I stumbled upon a highly controversial Aeon article titled “The Empty Brain: Your brain does not process information, retrieve knowledge or store memories. In short: your brain is not a computer” by psychologist Rob Epstein. This article attested to me once again just how wide the range of professional opinions may be when it comes to brain and mind in general. Unsurprisingly, the article drew an outrage from the reading audience. I myself disagree with the author on most fronts but one thing, I actually agree with him is that yes, our brains are not “digital computers.” They are, rather, neural networks where each neuron might function sort of like a quantum computer. The author has never offered his version of what human brains are like, but only criticized IT metaphors in his article. It’s my impression, that at the time of writing the psychologist hadn’t even come across such terms as neuromorphic computing, quantum computing, cognitive computing, deep learning, evolutionary computing, computational neuroscience, deep neural networks, and alike. All these IT concepts clearly indicate that today’s AI research and computer science derive their inspiration from human brain information processing — notably neuromorphic neural networks aspiring to incorporate quantum computing into AI cognitive architecture. Deep neural networks learn by doing just children.

By Alex Vikoulov.

Continue reading “Our Neural Code: A Pathway to AI Minds?” »

The human brain has amazing capabilities making it in many ways more powerful than the world’s most advanced computers. So it’s not surprising that engineers have long been trying to copy it. Today, artificial neural networks inspired by the structure of the brain are used to tackle some of the most difficult problems in artificial intelligence (AI). But this approach typically involves building software so information is processed in a similar way to the brain, rather than creating hardware that mimics neurons.

My colleagues and I instead hope to build the first dedicated neural network computer, using the latest “quantum” technology rather than AI software. By combining these two branches of computing, we hope to produce a breakthrough which leads to AI that operates at unprecedented speed, automatically making very complex decisions in a very short time.

We need much more advanced AI if we want it to help us create things like truly autonomous self-driving cars and systems for accurately managing the traffic flow of an entire city in real-time. Many attempts to build this kind of software involve writing code that mimics the way neurons in the human brain work and combining many of these artificial neurons into a network. Each neuron mimics a decision-making process by taking a number of input signals and processing them to give an output corresponding to either “yes” or “no”.

Continue reading “Quantum Computer: We’re Planning to Create One That Acts Like a Brain” »