Lifeboat Foundation

It’s difficult to simulate quantum physics, as the computing demand grows exponentially the more complex the quantum system gets — even a supercomputer might not be enough. AI might come to the rescue, though. Researchers have developed a computational method that uses neural networks to simulate quantum systems of “considerable” size, no matter what the geometry. To put it relatively simply, the team combines familiar methods of studying quantum systems (such as Monte Carlo random sampling) with a neural network that can simultaneously represent many quantum states.

The Born rule, which connects the math of quantum theory to the outcomes of experiments, has been derived from simpler physical principles. The new work promises to give researchers a better grip on the core mystery of quantum mechanics.

The quantum interference of electrons that have been scattered by light has been used to produce holograms of the underlying electromagnetic fields — and might open up methods for studying materials at high temporal and spatial resolution. A fresh approach to imaging light fields.

Jigang Wang and his collaborators have demonstrated light-induced acceleration of supercurrents, which could enable practical applications of quantum mechanics such as computing, sensing and communicating. Larger image. Image courtesy of Jigang Wang.

AMES, Iowa – Jigang Wang patiently explained his latest discovery in quantum control that could lead to superfast computing based on quantum mechanics: He mentioned light-induced superconductivity without energy gap. He brought up forbidden supercurrent quantum beats. And he mentioned terahertz-speed symmetry breaking.

Quantum physics will bring us even faster computers and tap-proof communication. However, there are still a number of problems to solve before the breakthrough. The prototype of a quantum interface, which was developed at the Institute for Science and Technology (IST) Austria, brings us one step closer to quantum internet. The transfer of information from one quantum computer to another becomes possible.

One problem with quantum computers is that the electronics only function at extremely low temperatures of a few thousands of a degree above absolute zero (−273.15 °C). If the temperature in the computer rises, all information is destroyed. The reason for this is superconductivity – a macroscopic quantum state of materials whose electrical resistance drops abruptly to zero when the temperature drops below the transition temperature. In the case of the quantum computer, these are microwave photons that are extremely sensitive to noise and losses.

This temperature sensitivity currently makes it almost impossible to transfer information from one quantum computer to another. The information would have to pass through an environment with high temperatures it could not survive in.

Researchers have come up with a way we could harvest energy from Earth by turning excess infrared radiation and waste heat into electricity we can use.

The concept involves the strange physics of quantum tunnelling, and key to the idea is a specially designed antenna that can detect waste or infrared heat as high-frequency electromagnetic waves, transforming these quadrillionth-of-a-second wave signals into a direct charge.

There’s actually a lot of energy going to waste here on Earth – most sunlight that hits the planet gets sucked up by surfaces, the oceans, and our atmosphere.

Chinese researchers are developing an airborne quantum communications network with drones as nodes.

Quantum supremacy sounds like something out of a Marvel movie. But for scientists working at the forefront of quantum computing, the hope—and hype—of this fundamentally different method of processing information is very real. Thanks to the quirky properties of quantum mechanics (here’s a nifty primer), quantum computers have the potential to massively speed up certain types of problems, particularly those that simulate nature.

Scientists are especially enthralled with the idea of marrying the quantum world with machine learning. Despite all their achievements, our silicon learning buddies remain handicapped: machine learning algorithms and traditional CPUs don’t play well, partly because the greedy algorithms tax classical computing hardware.

Add in a dose of quantum computing, however, and machine learning could potentially process complex problems beyond current abilities at a fraction of the time.

Oxidation numbers have so far eluded any rigorous quantum mechanical definition. A new SISSA study, published in Nature Physics, provides such a definition based on the theory of topological quantum numbers, which was honored with the 2016 Nobel Prize in Physics, awarded to Thouless, Haldane and Kosterlitz. This result, combined with recent advances in the theory of transport achieved at SISSA, paves the way to an accurate, yet tractable, numerical simulation of a broad class of materials that are important in energy-related technologies and planetary sciences.

Every undergraduate student in the natural sciences learns how to associate an integer oxidation number to a chemical species participating in a reaction. Unfortunately, the very concept of oxidation state has thus far eluded a rigorous quantum mechanical definition, so that no method was known until now to compute oxidation numbers from the fundamental laws of nature, let alone demonstrate that their use in the simulation of charge transport does not spoil the quality of numerical simulations. At the same time, the evaluation of electric currents in ionic conductors, which is required to model their transport properties, is presently based on a cumbersome quantum-mechanical approach that severely limits the feasibility of large-scale computer simulations. Scientists have lately noticed that a simplified model where each atom carries a charge equal to its oxidation number may give results in surprising good agreement with rigorous but much more expensive approaches.