Digital security currently relies on difficult equations to protect data. For example, when you use a credit card online, the information is locked inside a math problem that would take a modern computer thousands of years to solve. However, if someone builds a powerful enough computer, that security breaks.
To make systems safer, there is a major shift toward quantum security. This is where the unbreakable laws of quantum physics can be used to protect data instead.
Ruthenium dioxide (RuO2) is a metal oxide that commonly serves as an important metallic conductor, quantum material and industrial electrocatalyst. While there have been debates surrounding the magnetic properties of RuO2, it is generally thought to be nonmagnetic in its bulk form. But now, a new study, published in Science Advances, has found that very thin layers of RuO2 can become magnetic when its lattice is placed under strain.
RuO2 is known for its unusual magnetic properties, which have led some researchers to propose that it may be an altermagnet —a material with magnetic order but little or no overall magnetization. Most previous studies have examined bulk RuO2 crystals or thicker, relaxed films and have reported conflicting findings on magnetic order. Earlier studies linked RuO2 to magnetic order, unusual Hall effects and efficient spin-charge conversion, while later studies found no magnetic order in bulk or relaxed films.
While one study reported the absence of altermagnetic properties down to 5 nanometers of RuO2 thickness, a fully strained ultrathin regime below about 4 nanometers had not been directly studied with both momentum and spin resolution.
The American Physical Society’s newest highly selective, open access journal, PRX Intelligence, has published its inaugural papers. The studies demonstrate how artificial intelligence and machine learning methods can be used to advance scientific knowledge and capabilities across the physical sciences — from neural networks that streamline molecular simulations to data-driven learning schemes that accelerate quantum embedding workflows.
As AI and machine learning transform the physical sciences, PRX Intelligence is designed to provide a multidisciplinary platform for research pioneering the development and application of these approaches. Building on the foundation of Physical Review X, the journal publishes open access articles expected to have substantial and lasting impact. It welcomes studies that apply AI and machine learning across theory, simulation, and experimentation in physics and related fields — including computer science, mathematics, engineering, materials science, chemistry, biology, and earth and environmental sciences. Relevant topics include discovery and synthesis, physics-informed learning, data-driven approaches, machine learning pipelines for observational platforms, and more. Articles have flexible formats and lengths and may include research papers, perspectives, roadmaps, tutorials, and more.
PRX Intelligence will waive article publication charges for manuscripts submitted or transferred before Jan. 1, 2027. And like all other APS journals, it will always waive these charges for researchers in low-and middle-income countries. Sign up for email updates to keep up with the latest news from the journal.
A silicon quantum processing unit executes high-fidelity multiqubit circuits, with all time-varying control signals generated by a digitally programmed cryogenic complementary metal–oxide–semiconductor controller and delivered to the low-noise, exchange-only qubit device through a high-density superconducting ribbon cable.
Semiconductor spin qubits are one of the most promising building blocks for future quantum computers, but turning them into a working, large-scale quantum computer has so far proven difficult. For now, two big questions remain open: how to connect qubits that aren’t sitting right next to each other, and how to control huge numbers of them without an unmanageable tangle of wiring.
Now, two independent studies published in Nature have each reported new experiments tackling these problems head-on.
Researchers in the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign have discovered a new type of quantum light emitter in diamonds that could help overcome a number of challenges facing quantum technologies.
The research was led by ECE graduate student Swetapadma Sahoo in Assistant Professor Simeon Bogdanov’s research group, with contributions from undergraduate students Jaden Li and Darwon Kim. The Illinois team also collaborated with researchers from Oak Ridge National Laboratory, UCLA and international partners in France and Russia.
The findings, published in Nature Communications, introduce a newly identified diamond color center, named IL1 after the University of Illinois. The IL1 center emits exceptionally bright, narrowband quantum light consisting of single photons while remaining remarkably insensitive to the crystal vibrations typical in a diamond lattice.
A previously overlooked set of spin symmetries has revealed that magnetism does not fit neatly into just two categories.
For more than a century, physicists divided collinear magnets into two basic types. The discovery of altermagnetism has added a third, earning Professor Jairo Sinova of Johannes Gutenberg University Mainz (JGU), Dr. Libor Šmejkal and Professor Tomas Jungwirth the 2026 Europhysics Prize from the European Physical Society (EPS) Condensed Matter Division.
One of Europe’s leading honors in condensed matter physics, the prize recognizes their work establishing altermagnetism as a previously unknown fundamental form of magnetic order alongside ferromagnetism and antiferromagnetism. The finding has challenged a long-accepted picture of magnetism and created a research field with potential consequences for quantum materials, condensed matter physics and future information technologies.
How even small but frequent disruptions can cause quantum computers to fail.
Quantum computers are expected to tackle difficult tasks more quickly and with less energy than current supercomputers, including molecular simulations and complex logistics planning.
Progress toward that goal depends partly on increasing the number of qubits, the basic units that store and process quantum information. Yet scaling up may introduce a problem that has received far less attention.
