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Scientists with the University of Technology Sydney (UTS) and the University of New South Wales (UNSW) have developed a method that helps to fine-tune the control of particles using ultrasonic waves according to new research, which they say expands our understanding of the field of acoustic levitation.

The levitation of objects, once a phenomenon seen only in science fiction and fantasy, now represents a field in acoustics with practical applications in multiple research areas, industries, and even among hobbyists. However, the use of high-intensity sound waves to suspend small objects in the air is nothing new. The theoretical basis for overcoming gravity with the help of acoustic radiation pressure goes as far back as the 1930s, when researcher Louis King first studied the suspension of particles in the field of a sound wave, and how this demonstrates acoustic radiation force being exerted against them.

Later calculations beginning in the 1950s helped to further refine our understanding of the acoustic radiation force produced by the scattering of sound waves. However, the modern foundation for acoustic levitation science draws mainly from the work of superconductivity pioneer Lev. P. Gorkov, who was the first to synthesize previous studies and provide a solid mathematical basis for the phenomenon.

Researchers used deep reinforcement learning to steer atoms into a lattice shape, with a view to building new materials or nanodevices.

In a very cold vacuum chamber, single atoms of silver form a star-like lattice. The precise formation is not accidental, and it wasn’t constructed directly by human hands either. Researchers used a kind of artificial intelligence called deep reinforcement learning to steer the atoms, each a fraction of a nanometer in size, into the lattice shape. The process is similar to moving marbles around a Chinese checkers board, but with very tiny tweezers grabbing and dragging each atom into place.

The main application for deep reinforcement learning is in robotics, says postdoctoral researcher I-Ju Chen. “We’re also building robotic arms with deep learning, but for moving atoms,” she explains. “Reinforcement learning is successful in things like playing chess or video games, but we’ve applied it to solve technical problems at the nanoscale.”

This research could potentially lead to a better understanding of the galaxy and its many mysteries.

It’s a cosmic riddle: How can galaxies remain together when all the matter we observe isn’t enough to keep them intact? Scientists believe an invisible force must beat play, something so mysterious they named it “dark matter” because of its lack of visibility.

This mysterious presence accounts for nearly three times more than what we can observe — a startling 27% of all existence! The mysterious dark matter is a profound mystery to scientists, its existence making up nearly one-third of the universe’s energy and mass yet remaining elusive due to its ability to avoid detection.

Continue reading “Researchers plan to use quantum computers in search for dark matter” »

After recombining the superposed photons by sending them through another crystal, the team measured the photon polarization across a number of repeated experiments. They found a quantum interference pattern, a pattern of light and dark stripes that could exist only if the photon had been split and was moving in both time directions.

“The superposition of processes we realized is more akin to an object spinning clockwise and counter-clockwise at the same time,” Strömberg said. The researchers created their time-flipped photon out of intellectual curiosity, but follow-up experiments showed that time flips can be paired with reversible logic gates to enable simultaneous computation in either direction, thus opening the way for quantum processors with greatly enhanced processing power.

Theoretical possibilities also sprout from the work. A future theory of quantum gravity, which would unite general relativity and quantum mechanics, should include particles of mixed time orientations like the one in this experiment, and could enable the researchers to peer into some of the universe’s most mysterious phenomena.

The concept of ‘anti-realism’ is widely seen as a fact of life for many physicists studying the mysterious effects of quantum mechanics. However, it also seems to contradict the assumptions of many other fields of research. In his research, Dr William Sulis at McMaster University in Canada explores the issue from a new perspective, by using a novel mathematical toolset named the ‘process algebra model’. In suggesting that reality itself is generated by interacting processes more fundamental than quantum particles, his theories could improve researchers’ understanding of fundamental processes in a wide variety of fields.

The concept of ‘locality’ states that objects and processes can only be influenced by other objects and processes in their immediate surroundings. It is a fundamental aspect of many fields of research and underpins all of the most complex systems we observe in nature, including living organisms. “Biologists and psychologists have known for centuries that the physical world is dominated by processes which are characterized by factors including transformation, interdependence, and information”, Dr Sulis explains. “Organisms are born, develop, continually exchange physical components and information with their environment, and eventually die.”

Beyond biology, the principle of locality also extends to Einstein’s theory of special relativity. Since the speed of light sets a fundamental speed limit on all processes in the universe, the theory states that no process can occur if it has not been triggered by another event in its past, at a close enough distance for light to travel between them within the time separating them. In general, these theories are unified by a concept which physicists call ‘realism’. Yet despite this seemingly intuitive rule, physicists have increasingly come to accept the idea that it doesn’t present a full description of how all processes unfold.

Researchers at Tohoku University, the University of Messina, and the University of California, Santa Barbara (UCSB) have developed a scaled-up version of a probabilistic computer (p-computer) with stochastic spintronic devices that is suitable for hard computational problems like combinatorial optimization and machine learning.

Moore’s law predicts that computers get faster every two years because of the evolution of semiconductor chips. While this is what has historically happened, the continued evolution is starting to lag. The revolutions in machine learning and artificial intelligence means much higher computational ability is required. Quantum computing is one way of meeting these challenges, but significant hurdles to the practical realization of scalable quantum computers remain.

A p-computer harnesses naturally stochastic building blocks called probabilistic bits (p-bits). Unlike bits in traditional computers, p-bits oscillate between states. A p-computer can operate at room-temperature and acts as a domain-specific computer for a wide variety of applications in machine learning and artificial intelligence. Just like quantum computers try to solve inherently quantum problems in quantum chemistry, p-computers attempt to tackle probabilistic algorithms, widely used for complicated computational problems in combinatorial optimization and sampling.

In 1916, Einstein finished his Theory of General Relativity, which describes how gravitational forces alter the curvature of spacetime. Among other things, this theory predicted that the Universe is expanding, which was confirmed by the observations of Edwin Hubble in 1929. Since then, astronomers have looked farther into space (and hence, back in time) to measure how fast the Universe is expanding – aka. the Hubble Constant. These measurements have become increasingly accurate thanks to the discovery of the Cosmic Microwave Background (CMB) and observatories like the Hubble Space Telescope.

Astronomers have traditionally done this in two ways: directly measuring it locally (using variable stars and supernovae) and indirectly based on redshift measurements of the CMB and cosmological models. Unfortunately, these two methods have produced different values over the past decade. As a result, astronomers have been looking for a possible solution to this problem, known as the “Hubble Tension.” According to a new paper by a team of astrophysicists, the existence of “Early Dark Energy” may be the solution cosmologists have been looking for.

Continue reading “‘Early Dark Energy’ Could Explain the Crisis in Cosmology” »

Establishing a moon base will be critical for the U.S. in the new space race and building safe and cost-effective landing pads for spacecraft to touch down there will be key.

These pads will have to stop lunar dust and particles from sandblasting everything around them at more than 10,000 miles per hour as a rocket takes off or lands since there is no air to slow the rocket plume down.

However, how to build these landing pads is not so clear, as hauling materials and heavy equipment more than 230,000 miles into space quickly becomes cost prohibitive.

Dark matter makes up about 27% of the matter and energy budget in the universe, but scientists do not know much about it. They do know that it is cold, meaning that the particles that make up dark matter are slow-moving. It is also difficult to detect dark matter directly because it does not interact with light. However, scientists at the U.S. Department of Energy’s Fermi National Accelerator Laboratory (Fermilab) have discovered a way to use quantum computers to look for dark matter.

Aaron Chou, a senior scientist at Fermilab, works on detecting dark matter through quantum science. As part of DOE’s Office of High Energy Physics QuantISED program, he has developed a way to use qubits, the main component of quantum computing.

Performing computation using quantum-mechanical phenomena such as superposition and entanglement.