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Circa 2013 o.o


Quantum entanglement, one of the odder aspects of quantum theory, links the properties of particles even when they are separated by large distances. When a property of one of a pair of entangled particles is measured, the other “immediately” settles down into a state compatible with that measurement. So how fast is “immediately”? According to research by Prof. Juan Yin and colleagues at the University of Science and Technology of China in Shanghai, the lower limit to the speed associated with entanglement dynamics – or “spooky action at a distance” – is at least 10000 times faster than light.

Despite playing a vital role in the development of quantum theory, Einstein felt philosophically at odds with its description of how the universe works. His famous quote that “God does not play dice” hints at his level of discomfort with the role of probability in quantum theory. He believed there exists another level of reality in which all of physics would be deterministic, and that quantum mechanics would turn out to be a description that emerges from the workings of that level – rather like a traffic jam emerges from the independent motions of a large number of cars.

In 1935 Einstein and his coworkers discovered quantum entanglement lurking in the equations of quantum mechanics, and realized its utter strangeness. This lead to the EPR paradox introduced by Einstein, Poldolsky and Rosen. The EPR paradox stated that the only ways of explaining the effects of quantum entanglement were to assume the universe is nonlocal, or that the true basis of physics is hidden (otherwise known as a hidden-variable theory). Nonlocality in this case means that events occurring to entangled objects are linked even when the events cannot communicate through spacetime, spacetime having the speed of light as a limiting velocity. Nonlocality is also known as spooky action at a distance (Einstein’s phrase).

Each year, billions of tons of goods are transported globally using cargo containers. Currently, there are concerns that this immense volume of traffic could be exploited to transport illicit nuclear materials, with little chance of detection. One promising approach to combating this issue is to measure how goods interact with charged particles named muons—which form naturally as cosmic rays interact with Earth’s atmosphere. Studies worldwide have now explored how this technique, named “muon tomography,” can be achieved through a variety of detection technologies and reconstruction algorithms. In this article of EPJ Plus, a team headed by Francesco Riggi at the University of Catania, Italy, build on these results to develop a full-scale muon tomograph prototype.

Computer-aided calculations have played a crucial part in producing the proofs of several high-profile results. And more recently, some mathematicians have made progress towards AI that doesn’t just perform repetitive calculations, but develops its own proofs. Another growing area has been software that can go over a mathematical proof written by humans and check that it is correct.


Algorithm named after mathematician Srinivasa Ramanujan suggests interesting formulae, some of which are difficult to prove true.

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What is this mysterious quantum tunneling effect, where does it come from? And why is it one of the most important phenomena in physics?

Quantum mechanics shows that quantum objects have a wave-particle duality. What we think of as an electron particle actually behaves like a wave, a probability wave. This means that its position is not a precise location in space. It is defined by a wave function that can only tell us the probability of finding it a particular location when measured. The wave function of a particle exists in all of space, in the entire universe up to infinity. So there is always a non-zero probability of finding the electron anywhere, including outside a barrier.

For all its comparisons to the human brain, AI still isn’t much like us. Maybe that’s alright. In the animal kingdom, brains come in all shapes and sizes. So, in a new machine learning approach, engineers did away with the human brain and all its beautiful complexity—turning instead to the brain of a lowly worm for inspiration.

Turns out, simplicity has its benefits. The resulting neural network is efficient, transparent, and here’s the kicker: It’s a lifelong learner.

Whereas most machine learning algorithms can’t hone their skills beyond an initial training period, the researchers say the new approach, called a liquid neural network, has a kind of built-in “neuroplasticity.” That is, as it goes about its work—say, in the future, maybe driving a car or directing a robot—it can learn from experience and adjust its connections on the fly.

In a world that’s noisy and chaotic, such adaptability is essential. ## **Worm-Brained Driver**

The algorithm’s architecture was inspired by the mere 302 neurons making up the nervous system of *C. elegans*, a tiny nematode (or worm).