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Category: particle physics

AI helps reveal large-scale quantum effects hidden in stacked atomic sheets

Quantum materials are a class of exotic materials with special properties that are governed by quantum mechanics rather than classical physics. Those properties—like superconductivity, entanglement and unusual forms of magnetism—often originate in the tiny repeating patterns of atoms inside crystals, but through clever engineering, they can be observed and controlled at a more human scale. Quantum materials are helping to power the quickly growing field of quantum computing and could find their way into future generations of energy-efficient electronics.

Designing new materials from the atomic scale up, however, requires intense modeling and simulation. Some materials may appear ordinary when viewed as small clusters of atoms, yet reveal new and useful properties when their atomic building blocks repeat and interact over larger distances. Researchers must be able to accurately predict behaviors at large scales in order to find materials with practical applications—otherwise, designing new materials is a slow and costly trial-and-error process.

In the past 50 years, supercomputers have helped materials scientists solve some of those thorny prediction problems, but two recent studies from the University of Washington demonstrate how newer computing techniques can help researchers sniff out promising quantum materials to pursue.

An underground detector in China unveils its first major findings about mysterious ghost particles

A massive underground detector aimed at understanding the mysterious ghost particles in our universe released its first major results on Wednesday.

The Jiangmen Underground Neutrino Observatory in China started collecting data in August with the goal of understanding neutrinos: tiny cosmic particles that date back to the Big Bang and whiz harmlessly through our bodies by the trillions every second. Yet they weigh almost nothing, making them difficult to sniff out.

In a study published Wednesday in the journal Nature, the JUNO team unveiled its initial findings from two months of data collection—including some of the most precise measurements to date of how neutrinos switch between three varieties, or flavors, as they zip through space.

Quantum witness technique reveals spinons in quantum spin liquid candidate

Physicists at University College Cork have developed a new approach in the search for a quantum spin liquid, a long-sought state of quantum matter resembling a magnetic liquid whose quantum properties mean it never freezes. The work is a key step in the search for quantum silicon, a mineral that could be used to create quantum computers, just as silicon is used in traditional computers. The resulting paper appears in Nature Physics.

Lead author Prof. Seamus Davis said, “By introducing the quantum witness technique we provide a completely new perspective on the physics of quantum spin liquids and access their internal quantum excitations or ‘spinons’ directly for the first time at UCC.”

As liquids cool, they freeze into solids as their atoms cease to move. But some liquids, such as helium, never freeze. Predominant quantum effects mean they flow as superfluids even at absolute zero (the coldest possible temperature).

Attosecond interferometry meets quantum optics

Experimental attosecond science is built around the ability to generate and control light flashes lasting billionths of a billionth of a second. Such extreme pulses can be created through high harmonic generation (HHG), where an intense laser field drives electrons out of atoms or solids and then forces them back, releasing bursts of extreme ultraviolet radiation. Techniques like this have transformed our ability to observe electron motion on its natural timescale.

To extract information from such ultrafast processes, physicists often rely on attosecond interferometry. By combining a strong laser field with a weaker second colour, different electron trajectories are made to interfere, imprinting timing and phase information onto the emitted harmonics. Over recent years, these schemes have become standard tools for attosecond metrology and spectroscopy.

To discover new physics, AI may need to ‘unlearn’ the old one

A study in the Journal of Cosmology and Astroparticle Physics explores how a machine-learning strategy known as transfer learning could dramatically reduce the computational cost of searching for new physics beyond the standard cosmological model—while also revealing an unexpected risk: Sometimes AI systems can become too reliant on what they already know.

Artificial intelligence is widely used in cosmology to analyze the universe. But testing theories beyond the standard cosmological model, known as ΛCDM, remains extremely computationally demanding.

Although ΛCDM successfully describes many properties of the universe—from its expansion to the distribution of galaxies—physicists know it is probably incomplete. Recent observations hint that phenomena such as massive neutrinos, modified gravity or evolving dark energy could point toward new physics beyond the current model.

Monolayer WSe₂ unlocks high-performance p-type transistors that could change how future chips balance speed and power

Transistors, small devices that can amplify or switch electrical signals, are central components of all modern computer chips and digital devices. There are two main types of transistors, known as n-type and p-type transistors.

N-type transistors conduct current using electrons (i.e., negatively charged particles), while p-type transistors utilize electron holes (i.e., positively charged spaces in a crystal lattice without electrons).

Electronics engineers worldwide have been exploring different solutions that could help reduce the size of existing transistors without compromising their performance, which could enable the further miniaturization of electronic devices. One promising route is to fabricate transistors using two-dimensional (2D) semiconductors, semiconducting materials that are just a single atom or a few atoms thick.

Neutron-rich nuclei yield beta-decay clues that could refine heavy-element origin models

How are heavy elements formed in the universe? Extremely neutron-rich atomic nuclei and their beta-decay rates play an important role in this process. Until now, it has been very difficult to determine these rates experimentally. Researchers at TU Darmstadt have developed theoretical predictions for such processes and successfully compared them with experimental data, where they exist. The results were published in Physical Review Letters.

The study focuses on beta-decay rates of neutron-rich nuclei, which are of great importance for element synthesis in the universe. To better understand and predict these decay rates, the team developed modern “ab initio” methods in nuclear physics for these systems. These methods calculate the properties of atomic nuclei directly from the fundamental interactions between their constituents, without making empirical adjustments to known measured values.

The researchers combined modern nuclear forces and decay operators with many-particle methods to precisely determine the structure of nuclei and, from this, the decay rates. A key finding of the work is that the theoretical predictions agree very well with experimental data—in the range where such extremely neutron-rich nuclei can currently be studied at accelerator facilities. The latest experiments on these nuclei took place at the RIKEN research center in Japan.

Scientists identify the origin of noise in spin qubit quantum processors

A spin qubit, in which quantum information is encoded in the spin state of an electron, is one of the most promising platforms for quantum computing. Spin qubits exhibit long coherence times and are compatible with advanced semiconductor manufacturing technologies. The leading implementation of spin qubits involves confined electrons inside quantum dots, a nanoscale semiconductor architecture that behaves like a controllable artificial atom. Recent advances have enabled high-fidelity operation of single- and two-qubit gates, exceeding the threshold required for certain surface code quantum error correction techniques.

Physicists create new family of Schrödinger-cat states

Quantum mechanics, unlike classical physics, allows objects to exist in more than one state at the same time. This idea is often illustrated by Schrödinger’s cat, imagined as being both alive and dead until it is observed. In the laboratory, physicists can create less dramatic but very real versions of this effect by placing atoms, light or motion into two distinct quantum states at once. Creating and controlling these superpositions is essential for applications ranging from quantum computing to precision timekeeping.

A simple example is a quantum bit, or qubit, in a superposition of both 0 and 1. But quantum systems are not limited to just two states. In a quantum harmonic oscillator, which can occupy many different energy levels, there is a much richer set of possibilities. Quantum harmonic oscillators describe many physical systems, including light, vibrations and the motion of trapped particles, and have been used to create a wide variety of quantum superpositions. One well-known example is a “cat state,” in which an oscillator is placed in a superposition of two wave packets displaced in opposite directions. These wave packets, known as coherent states, resemble classical motion as closely as quantum mechanics allows.

Researchers at the University of Oxford have now demonstrated a new family of quantum superpositions. Instead of building catlike states from coherent-state wave packets, they developed a method for creating superpositions from a broad range of components that are themselves highly nonclassical. In examples such as squeezed-state superpositions, quantum uncertainty is redistributed differently in each part of the state. The research is published in the journal Physical Review X.

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