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Category: quantum physics – Page 48

Nickelate superconductors share a common electronic fingerprint

Superconductors, materials that conduct electricity with zero electrical resistance at specific temperature ranges, have proved very promising for the development of quantum computers and other cutting-edge technologies. While most of these materials become superconducting at very low temperatures, others exhibit superconductivity at higher temperatures.

Two types of materials that are known to be high-temperature semiconductors are cuprates (i.e., compounds containing negatively charged copper ions) and nickelates (i.e., compounds that contain negatively charged nickel-oxygen ions). While cuprates have been known to be superconductors for decades, nickelates were only recently found to exhibit superconductivity at unusually high temperatures.

Researchers at University of British Columbia (UBC), Argonne National Laboratory, and the Canadian Light Source (CLS), carried out a study aimed at better understanding how the electronic structure of nickelates contributes to their superconductivity.

Tabletop experiment helps reconcile fundamental physics

Assistant Professor Haocun Yu is something of a scientific diplomat. In a recent Physical Review Letters publication, she and her colleagues show how a tabletop experiment can bring together two bedrock physics theories that have never been fully reconciled.

More than a century ago, Albert Einstein gave us the theory of general relativity, describing gravity in relation to space and time on a large scale. Within a decade, physicists were developing a deeper knowledge of quantum mechanics, the laws that govern the subatomic world, including atoms, photons and other microscopic systems.

“Quantum mechanics and general relativity are two of the most successful theories in physics, but they describe nature in very different ways,” Yu explained.

Research uncovers novel electronic properties in quantum material

Florida State University physicists are part of a team that has discovered unusual superconducting states in parts of graphene, with the potential to drive unexpected quantum technologies.

Assistant Professor of Physics Cyprian Lewandowski and postdoctoral researcher Phong Võ Tiến are part of an international collaboration that has uncovered new aspects of superconductivity and topology in rhombohedral graphene, a system comprising just a few layers of carbon atoms stacked like the treads of a staircase in a shape known as chiral stacking. The work is published in Nature Physics.

“The rhombohedral graphene system seems to capture many of the intriguing electronic phenomena that scientists have seen previously in other atomically thin systems, but they were previously not as ideal for technical applications because of the intrinsic complexity of the devices or replicability issues,” Lewandowski said.

Quantum circuits help AI overcome memory limitations with minimal new parameters

For millions of people, chatbots powered by large language models (LLMs) are now a key feature of everyday life. These AI systems are growing at a rapid pace, but scaling them up is becoming increasingly costly and resource-intensive.

Through a new preprint on the arXiv server, a team led by Borja Aizpurua at Multiverse Computing in San Sebastián, Spain, has found a way to improve the performance of LLMs using quantum computing. Their approach could offer a smarter alternative, rather than simply throwing more hardware at the problem.

Predictive surrogates could cut quantum computing measurement overhead by more than 99.97%

Quantum computers, systems that process information leveraging quantum mechanical effects, have the potential of outperforming classical computers on some tasks. Despite their potential, the use of these systems remains very limited, due to their high cost and other challenges that have so far prevented their large-scale fabrication.

Researchers at the Henan Key Laboratory of Quantum Information and Cryptography and Nanyang Technological University have developed predictive surrogates, new computational models that can learn and reproduce the outputs of quantum processors.

These models, introduced in a paper published in Nature Communications, could be used to extract useful information from quantum computers and perform computations more efficiently with provable guarantees, even if users do not have direct access to advanced and expensive quantum computing hardware.

Physics-trained digital ‘super-brain’ speeds nanophotonic design

Studying physics can be very useful—even when it comes to machine learning. A digital “super-brain” with built-in knowledge of the fundamental laws of nature can speed up the development of optical components for everything from quantum computers to eyeglasses or camera lenses, according to a new study from Chalmers University of Technology in Sweden.

“When we fed the super-brain information about the laws of physics, it immediately got much smarter. Our calculations now take one tenth of the time previously required,” says Philippe Tassin, professor at the Department of Physics and Astronomy, Chalmers University of Technology.

The research team led by Tassin designs optical components in a field called nanophotonics. On a small scale—less than one wavelength—light can be controlled and manipulated in a completely different way than on larger scales. But there are also limitations on how light can be controlled in advanced ways in natural optical materials.

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