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Light beam ‘swims’ upstream through a quantum fluid by violating Newton’s third law
Just as a leaf drifts along with a stream, objects in other moving fluids normally drift along with the flow. That is, unless they exert energy to move against it. Although it may be less intuitive, light waves or photons work similarly. To move against a stream of light, an object or particle, like a photon, must either have an external force acting on it or actively use energy to move upstream.
In a new study, published in Physical Review A, a team of physicists demonstrates how a beam of light can “swim” upstream in a quantum fluid of light by breaking action-reaction symmetry and reshaping how the surrounding forces affect the flow.
Astronomers detect radio signals coming from an exoplanet for the first time
Exoplanets are exotic worlds orbiting distant stars far beyond our solar system. Ever since the first ones were discovered in the 1990s, astronomers have turned their attention to these distant worlds to understand more about them. Now, for the first time, scientists have detected radio signals coming directly from one of these planets, a massive gas giant named β Pictoris b.
Previous radio detections from exoplanetary systems couldn’t be traced directly to the planet itself because astronomers couldn’t tell whether the signal came from the star or the planet. And no, this isn’t evidence of alien life communicating with each other. The radio waves come from auroras linked to the planet’s powerful magnetic field.
Auroras can occur when high-energy charged particles travel along a planet’s magnetic field lines and interact with its upper atmosphere. On Earth, for example, this produces the northern lights.
What billiard balls reveal about computers and the limits of prediction
When is a billiard ball not a billiard ball? When mathematicians get involved and view a popular game as a model computer.
When you send a ball across a table and it hits the sides, it follows simple physical rules. However, mathematicians see a particle (the ball) whose motion can represent information, while each bounce can help guide it through a calculation.
For years, mathematicians have wondered whether billiards are capable of universal computation—in other words, whether a simple two-dimensional billiard system can run any computer program imaginable.
Quantum protocol securely verifies a device’s position using stations 2 km apart
Reliably verifying the location of a device connected to the internet or other networks is important for various real-world applications. For instance, it could be valuable for authorizing financial transactions, securing communications and controlling who can access specific databases or services.
Some current methods used to verify a device’s position can be deceived using various techniques, such as GPS spoofing, manipulation of location data and relay attacks. These techniques allow attackers to transmit counterfeit satellite-navigation signals, alter software-reported GPS coordinates or intercept and forward verification messages, respectively.
Researchers at the University of Science and Technology of China recently developed a quantum position-verification protocol that could securely and reliably confirm the location of devices in a network. Their protocol, introduced in a paper published in Nature Physics, successfully authenticated a device’s position using two verifiers separated by 2 km (1.2 miles), narrowing its possible location to a range of 74.3 meters (244 feet).
Tiny quantum nanostructures could make AI less of an energy hog
Engineers at the University of Wisconsin–Madison have designed a new type of quantum nanostructure that could enable optical neural networks. This emerging technology has the potential to make artificial intelligence systems, like large language models and image generation, faster and significantly more energy efficient.
The research, led by electrical and computer engineering Ph.D. students Qingyi Zhou and Jungmin Kim, computer science Ph.D. student Yutian Tao, and Zongfu Yu, a professor of electrical and computer engineering, was published in the journal Nature Communications on Aug. 27.
Many of the most popular AI systems are based on deep neural networks, multilayer systems that mimic the interconnectedness of the human brain. As those systems scale in size and complexity, their energy consumption also increases. That’s one factor in recent concerns about AI energy use and data center construction.
RHIC data reveal intriguing dip in momentum fluctuations in high-density nuclear matter
Scientists using the STAR detector to study particle collisions at the Relativistic Heavy Ion Collider (RHIC) have found an intriguing dip in their data in a relatively unexplored region of the nuclear phase diagram—a map of how nuclear matter behaves under various conditions of temperature and density. The dip appears in data tracking collision-by-collision variations in the momenta of particles emerging from collisions between gold nuclei at RHIC, near RHIC’s lowest collision energies. These collisions create the highest-density nuclear matter and may indicate that something interesting is happening in that dense region of the phase diagram. The findings are described in a paper just published in Physical Review Letters.
RHIC, which operated as a U.S. Department of Energy (DOE) Office of Science user facility for nuclear physics research at DOE’s Brookhaven National Laboratory from 2000 to 2026, was designed to create exotic forms of matter, including the quark-gluon plasma that existed in the very early universe and matter that approaches the density of neutron stars. A dip in the momentum fluctuations—which are closely tied to the temperature of the matter—may be a sign that the way nuclei transform into these exotic substances changes character at RHIC’s lower energies.
Exploring whether such a change in transition behavior exists—and, if so, where a hypothesized “critical point” demarcating this change is located on the nuclear phase diagram—has been a long-sought goal of physicists conducting research at RHIC.
Carbon nanotube foams reveal a new kind of mechanical memory
In their earliest years of development, computers used mechanical gears and levers to store information. Today, researchers are exploring whether a material itself can hold onto information, storing memory in how it bends and springs back to its original shape.
Through new research published in Physical Review X, Ramathasan Thevamaran and colleagues at the University of Wisconsin–Madison have discovered a material that takes this concept a step further: a foam made of carbon nanotubes that remembers exactly how hard it was squeezed, then returns to its original shape with no lasting damage.