Computers Math

How can data be processed at lightning speed, or electricity conducted without loss? To achieve this, scientists and industry alike are turning to quantum materials, governed by the laws of the infinitesimal. Designing such materials requires a detailed understanding of atomic phenomena, much of which remains unexplored. A team from the University of Geneva (UNIGE), in collaboration with the University of Salerno and the CNR-SPIN Institute (Italy), has taken a major step forward by uncovering a hidden geometry — until now purely theoretical — that distorts the trajectories of electrons in much the same way gravity bends the path of light. This work, published in Science, opens new avenues for quantum electronics.
Future technologies depend on high-performance materials with unprecedented properties, rooted in quantum physics. At the heart of this revolution lies the study of matter at the microscopic scale — the very essence of quantum physics. In the past century, exploring atoms, electrons and photons within materials gave rise to transistors and, ultimately, to modern computing.
New quantum phenomena that defy established models are still being discovered today. Recent studies suggest the possible emergence of a geometry within certain materials when vast numbers of particles are observed. This geometry appears to distort the trajectories of electrons in these materials — much like Einstein’s gravity bends the path of light.
From theory to observation
Known as quantum metric, this geometry reflects the curvature of the quantum space in which electrons move. It plays a crucial role in many phenomena at the microscopic scale of matter. Yet detecting its presence and effects remains a major challenge.
”The concept of quantum metric dates back about 20 years, but for a long time it was regarded purely as a theoretical construct. Only in recent years have scientists begun to explore its tangible effects on the properties of matter,” explains Andrea Caviglia, full professor and director of the Department of Quantum Matter Physics at the UNIGE Faculty of Science.
Thanks to recent work, the team led by the UNIGE researcher, in collaboration with Carmine Ortix, associate professor in the Department of Physics at the University of Salerno, has detected quantum metric at the interface between two oxides — strontium titanate and lanthanum aluminate — a well-known quantum material. ”Its presence can be revealed by observing how electron trajectories are distorted under the combined influence of quantum metric and intense magnetic fields applied to solids,” explains Giacomo Sala, research associate in the Department of Quantum Matter Physics at the UNIGE Faculty of Science and lead author of the study.
Unlocking Future Technologies
Observing this phenomenon makes it possible to characterise a material’s optical, electronic and transport properties with greater precision. The research team also demonstrates that quantum metric is an intrinsic property of many materials — contrary to previous assumptions.
”These discoveries open up new avenues for exploring and harnessing quantum geometry in a wide range of materials, with major implications for future electronics operating at terahertz frequencies (a trillion hertz), as well as for superconductivity and light-matter interactions,” concludes Andrea Caviglia. More

Osaka, Japan — A joint research team from Japan has observed “heavy fermions,” electrons with dramatically enhanced mass, exhibiting quantum entanglement governed by the Planckian time – the fundamental unit of time in quantum mechanics. This discovery opens up exciting possibilities for harnessing this phenomenon in solid-state materials to develop a new type of quantum computer.
Heavy fermions arise when conduction electrons in a solid interact strongly with localized magnetic electrons, effectively increasing their mass. This phenomenon leads to unusual properties like unconventional superconductivity and is a central theme in condensed matter physics. Cerium-Rhodium-Tin (CeRhSn), the material studied in this research, belongs to a class of heavy fermion systems with a quasi-kagome lattice structure, known for its geometrical frustration effects.
Researchers investigated the electronic state of CeRhSn, known for exhibiting non-Fermi liquid behavior at relatively high temperatures. Precise measurements of CeRhSn’s reflectance spectra revealed non-Fermi liquid behavior persisting up to near room temperature, with heavy electron lifetimes approaching the Planckian limit. The observed spectral behavior, describable by a single function, strongly indicates that heavy electrons in CeRhSn are quantum entangled.
Dr. Shin-ichi Kimura of The University of Osaka, who led the research, explains, “Our findings demonstrate that heavy fermions in this quantum critical state are indeed entangled, and this entanglement is controlled by the Planckian time. This direct observation is a significant step towards understanding the complex interplay between quantum entanglement and heavy fermion behavior.”
Quantum entanglement is a key resource for quantum computing, and the ability to control and manipulate it in solid-state materials like CeRhSn offers a potential pathway towards novel quantum computing architectures. The Planckian time limit observed in this study provides crucial information for designing such systems. Further research into these entangled states could revolutionize quantum information processing and unlock new possibilities in quantum technologies. This discovery not only advances our understanding of strongly correlated electron systems but also paves the way for potential applications in next-generation quantum technologies. More

When genetic testing reveals a rare DNA mutation, doctors and patients are frequently left in the dark about what it actually means. Now, researchers at the Icahn School of Medicine at Mount Sinai have developed a powerful new way to determine whether a patient with a mutation is likely to actually develop disease, a concept known in genetics as penetrance.
The team set out to solve this problem using artificial intelligence (AI) and routine lab tests like cholesterol, blood counts, and kidney function. Details of the findings were reported in the August 28 online issue of Science. Their new method combines machine learning with electronic health records to offer a more accurate, data-driven view of genetic risk.
Traditional genetic studies often rely on a simple yes/no diagnosis to classify patients. But many diseases, like high blood pressure, diabetes, or cancer, don’t fit neatly into binary categories. The Mount Sinai researchers trained AI models to quantify disease on a spectrum, offering more nuanced insight into how disease risk plays out in real life.
“We wanted to move beyond black-and-white answers that often leave patients and providers uncertain about what a genetic test result actually means,” says Ron Do, PhD, senior study author and the Charles Bronfman Professor in Personalized Medicine at the Icahn School of Medicine at Mount Sinai. “By using artificial intelligence and real-world lab data, such as cholesterol levels or blood counts that are already part of most medical records, we can now better estimate how likely disease will develop in an individual with a specific genetic variant. It’s a much more nuanced, scalable, and accessible way to support precision medicine, especially when dealing with rare or ambiguous findings.”
Using more than 1 million electronic health records, the researchers built AI models for 10 common diseases. They then applied these models to people known to have rare genetic variants, generating a score between 0 and 1 that reflects the likelihood of developing the disease.
A higher score, closer to 1, suggests a variant may be more likely to contribute to disease, while a lower score indicates minimal or no risk. The team calculated “ML penetrance” scores for more than 1,600 genetic variants.
Some of the results were surprising, say the investigators. Variants previously labeled as “uncertain” showed clear disease signals, while others thought to cause disease had little effect in real-world data.

“While our AI model is not meant to replace clinical judgment, it can potentially serve as an important guide, especially when test results are unclear. Doctors could in the future use the ML penetrance score to decide whether patients should receive earlier screenings or take preventive steps, or to avoid unnecessary worry or intervention if the variant is low-risk,” says lead study author Iain S. Forrest, MD, PhD, in the lab of Dr. Do at the Icahn School of Medicine at Mount Sinai. “If a patient has a rare variant associated with Lynch syndrome, for instance, and it scores high, that could trigger earlier cancer screening, but if the risk appears low, jumping to conclusions or overtreatment might be avoided.”
The team is now working to expand the model to include more diseases, a wider range of genetic changes, and more diverse populations. They also plan to track how well these predictions hold up over time, whether people with high-risk variants actually go on to develop disease, and whether early action can make a difference.
“Ultimately, our study points to a potential future where AI and routine clinical data work hand in hand to provide more personalized, actionable insights for patients and families navigating genetic test results,” says Dr. Do. “Our hope is that this becomes a scalable way to support better decisions, clearer communication, and more confidence in what genetic information really means.”
The paper is titled “Machine learning-based penetrance of genetic variants.”
The study’s authors, as listed in the journal, are Iain S. Forrest, Ha My T. Vy, Ghislain Rocheleau, Daniel M. Jordan, Ben O. Petrazzini, Girish N. Nadkarni, Judy H. Cho, Mythily Ganapathi, Kuan-Lin Huang, Wendy K. Chung, and Ron Do.
This work was supported in part by the following grants: National Institute of General Medical Sciences of the National Institutes of Health (NIH) (T32-GM007280); the National Institute of General Medical Sciences of the NIH (R35-GM124836); the National Institute of Diabetes and Digestive and Kidney Diseases (U24-DK062429); the National Human Genome Research Institute of the NIH (R01-HG010365); the National Institute of General Medical Sciences of the NIH (R35-GM138113); and the National Institute of Diabetes and Digestive and Kidney Diseases (U24-DK062429).
* Mount Sinai Health System member hospitals: The Mount Sinai Hospital; Mount Sinai Brooklyn; Mount Sinai Morningside; Mount Sinai Queens; Mount Sinai South Nassau; Mount Sinai West; and New York Eye and Ear Infirmary of Mount Sinai More

Quantum networks are often described as the future of the internet — but instead of transmitting classical information in bits, they send quantum information carried by photons. These networks could enable ultra-secure communication, link together distant quantum computers into a single, vastly more powerful machine, and create precision sensing systems that can measure time or environmental conditions with unprecedented accuracy.
To make such a network possible, so-called quantum network nodes — that can store quantum information and share it via light particles – are needed. In their latest work, the Innsbruck team led by Ben Lanyon at the Department of Experimental Physics of the University of Innsbruck demonstrated such a node using a string of ten calcium ions in a prototype quantum computer. By carefully adjusting electric fields, the ions were moved one by one into an optical cavity. There, a finely tuned laser pulse triggered the emission of a single photon whose polarization was entangled with the ion’s state.
The process created a stream of photons; each tied to a different ion-qubit in the register. In future the photons could travel to distant nodes and be used to establish entanglement between separate quantum devices. The researchers achieved an average ion-photon entanglement fidelity of 92 percent, a level of precision that underscores the robustness of their method.
“One of the key strengths of this technique is its scalability,” says Ben Lanyon. “While earlier experiments managed to link only two or three ion-qubits to individual photons, the Innsbruck setup can be extended to much larger registers, potentially containing hundreds of ions and more.” This paves the way for connecting entire quantum processors across laboratories or even continents.
“Our method is a step towards building larger and more complex quantum networks,” says Marco Canteri, the first author of the study. “It brings us closer to practical applications such as quantum-secure communication, distributed quantum computing and large-scale distributed quantum sensing.”
Beyond networking, the technology could also advance optical atomic clocks, which keep time so precisely that they would lose less than a second over the age of the universe. Such clocks could be linked via quantum networks to form a worldwide timekeeping system of unmatched accuracy.
The work, now published in Physical Review Letters, was financially supported by the Austrian Science Fund FWF and the European Union, among others, and demonstrates not only a technical milestone but also a key building block for the next generation of quantum technologies. More

Researchers at Rice University and collaborating institutions have discovered direct evidence of active flat electronic bands in a kagome superconductor. This breakthrough could pave the way for new methods to design quantum materials — including superconductors, topological insulators and spin-based electronics — that could power future electronics and computing technologies. The study, published in Nature Communications Aug. 14, centers on the chromium-based kagome metal CsCr₃Sb₅, which becomes superconducting under pressure.
Kagome metals, characterized by their two-dimensional lattices of corner-sharing triangles, have recently been predicted to host compact molecular orbitals, or standing-wave patterns of electrons that could potentially facilitate unconventional superconductivity and novel magnetic orders that can be made active by electron correlation effects. In most materials, these flat bands remain too far from active energy levels to have any significant impact; however, in CsCr₃Sb₅, they are actively involved and directly influence the material’s properties.
Pengcheng Dai, Ming Yi and Qimiao Si of Rice’s Department of Physics and Astronomy and Smalley-Curl Institute, along with Di-Jing Huang of Taiwan’s National Synchrotron Radiation Research Center, led the study.
“Our results confirm a surprising theoretical prediction and establish a pathway for engineering exotic superconductivity through chemical and structural control,” said Dai, the Sam and Helen Worden Professor of Physics and Astronomy.
The finding provides experimental proof for ideas that had only existed in theoretical models. It also shows how the intricate geometry of kagome lattices can be used as a design tool for controlling the behavior of electrons in solids.
“By identifying active flat bands, we’ve demonstrated a direct connection between lattice geometry and emergent quantum states,” said Yi, an associate professor of physics and astronomy.
The research team employed two advanced synchrotron techniques alongside theoretical modeling to investigate the presence of active standing-wave electron modes. They used angle-resolved photoemission spectroscopy (ARPES) to map electrons emitted under synchrotron light, revealing distinct signatures associated with compact molecular orbitals. Resonant inelastic X-ray scattering (RIXS) measured magnetic excitations linked to these electronic modes.

“The ARPES and RIXS results of our collaborative team give a consistent picture that flat bands here are not passive spectators but active participants in shaping the magnetic and electronic landscape,” said Si, the Harry C. and Olga K. Wiess Professor of Physics and Astronomy, “This is amazing to see given that, until now, we were only able to see such features in abstract theoretical models.”
Theoretical support was provided by analyzing the effect of strong correlations starting from a custom-built electronic lattice model, which replicated the observed features and guided the interpretation of results. Fang Xie, a Rice Academy Junior Fellow and co-first author, led that portion of the study.
Obtaining such precise data required unusually large and pure crystals of CsCr₃Sb₅, synthesized using a refined method that produced samples 100 times larger than previous efforts, said Zehao Wang, a Rice graduate student and co-first author.
The work underscores the potential of interdisciplinary research across fields of study, said Yucheng Guo, a Rice graduate student and co-first author who led the ARPES work.
“This work was possible due to the collaboration that consisted of materials design, synthesis, electron and magnetic spectroscopy characterization and theory,” Guo said.
Co-authors from Rice include Yuefei Huang, Bin Gao, Ji Seop Oh, Han Wu, Zheng Ren, Yuan Fang, Yiming Wang, Ananya Biswas, Yichen Zhang, Ziqin Yue, Boris Yakobson and Junichiro Kono.
Other contributors include Hsiao-Yu Huang, Jun Okamoto, Ganesha Channagowdra, Atsushi Fujimori and Chien-Te Chen of Taiwan’s National Synchrotron Radiation Research Center; Xingye Lu of Beijing Normal University; Zhaoyu Liu and Jiun-Haw Chu of the University of Washington; Cheng Hu, Chris Jozwiak, Aaron Bostwick and Eli Rotenberg of the Lawrence Berkeley National Laboratory; Makoto Hashimoto and Donghui Lu of the SLAC National Accelerator Laboratory; Robert Birgeneau of the University of California, Berkeley; and Guang-Han Cao of Zhejiang University.
The U.S. Department of Energy, Robert A. Welch Foundation, Gordon and Betty Moore Foundation, Air Force Office of Scientific Research, National Science Foundation and Vannevar Bush Faculty Fellowship program supported this study. More

While conventional computers store information in the form of bits, fundamental pieces of logic that take a value of either 0 or 1, quantum computers are based on qubits. These can have a state that is simultaneously both 0 and 1. This odd property, a quirk of quantum physics known as superposition, lies at the heart of quantum computing’s promise to ultimately solve problems that are intractable for classical computers.
Many existing quantum computers are based on superconducting electronic systems in which electrons flow without resistance at extremely low temperatures. In these systems, the quantum mechanical nature of electrons flowing through carefully designed resonators creates superconducting qubits. These qubits are excellent at quickly performing the logical operations needed for computing. However, storing information — in this case quantum states, mathematical descriptors of particular quantum systems — is not their strong suit. Quantum engineers have been seeking a way to boost the storage times of quantum states by constructing so-called “quantum memories” for superconducting qubits.
Now a team of Caltech scientists has used a hybrid approach for quantum memories, effectively translating electrical information into sound so that quantum states from superconducting qubits can survive in storage for a period up to 30 times longer than in other techniques.
The new work, led by Caltech graduate students Alkim Bozkurt and Omid Golami, supervised by Mohammad Mirhosseini, assistant professor of electrical engineering and applied physics, appears in a paper published in the journal Nature Physics.
“Once you have a quantum state, you might not want to do anything with it immediately,” Mirhosseini says. “You need to have a way to come back to it when you do want to do a logical operation. For that, you need a quantum memory.”
Previously, Mirhosseini’s group showed that sound, specifically phonons, which are individual particles of vibration (in the way that photons are individual particles of light) could provide a convenient method for storing quantum information. The devices they tested in classical experiments seemed ideal for pairing with superconducting qubits because they worked at the same extremely high gigahertz frequencies (humans hear at hertz and kilohertz frequencies that are at least a million times slower). They also performed well at the low temperatures needed to preserve quantum states with superconducting qubits and had long lifetimes.
Now Mirhosseini and his colleagues have fabricated a superconducting qubit on a chip and connected it to a tiny device that scientists call a mechanical oscillator. Essentially a miniature tuning fork, the oscillator consists of flexible plates that are vibrated by sound waves at gigahertz frequencies. When an electric charge is placed on those plates, the plates can interact with electrical signals carrying quantum information. This allows information to be piped into the device for storage as a “memory” and be piped out, or “remembered,” later.

The researchers carefully measured how long it took for the oscillator to lose its valuable quantum content once information entered the device. “It turns out that these oscillators have a lifetime about 30 times longer than the best superconducting qubits out there,” Mirhosseini says.
This method of constructing a quantum memory offers several advantages over previous strategies. Acoustic waves travel much slower than electromagnetic waves, enabling much more compact devices. Moreover, mechanical vibrations, unlike electromagnetic waves, do not propagate in free space, which means that energy does not leak out of the system. This allows for extended storage times and mitigates undesirable energy exchange between nearby devices. These advantages point to the possibility that many such tuning forks could be included in a single chip, providing a potentially scalable way of making quantum memories.
Mirhosseini says this work has demonstrated the minimum amount of interaction between electromagnetic and acoustic waves needed to probe the value of this hybrid system for use as a memory element. “For this platform to be truly useful for quantum computing, you need to be able to put quantum data in the system and take it out much faster. And that means that we have to find ways of increasing the interaction rate by a factor of three to 10 beyond what our current system is capable of,” Mirhosseini says. Luckily, his group has ideas about how that can be done.
Additional authors of the paper, “A mechanical quantum memory for microwave photons” are Yue Yu, a former visiting undergraduate student in the Mirhosseini lab; and Hao Tian, an Institute for Quantum Information and Matter postdoctoral scholar research associate in electrical engineering at Caltech. The work was supported by funding from the Air Force Office of Scientific Research and the National Science Foundation. Bozkurt was supported by an Eddleman Graduate Fellowship. More

The research, published in the academic journal Nature, represents an essential step in quantum computing and demonstrates its potential by directly simulating fundamental interactions with Google’s quantum processor. In the future, researchers could use this approach to gain deeper insights into particle physics, quantum materials, and even the nature of space and time itself. The aim is to understand how nature works at its most fundamental level, described by so-called gauge theories.
“Our work shows how quantum computers can help us explore the fundamental rules that govern our universe,” says co-author Michael Knap, Professor of Collective Quantum Dynamics at the TUM School of Natural Sciences. “By simulating these interactions in the laboratory, we can test theories in new ways.”
Pedram Roushan, co-author of this work from Google Quantum AI emphasizes: “Harnessing the power of the quantum processor, we studied the dynamics of a specific type of gauge theory and observed how particles and the invisible ‘strings’ that connect them evolve over time.”
Tyler Cochran, first author and graduate student at Princeton, says: “By adjusting effective parameters in the model, we could tune properties of the strings. They can fluctuate strongly, become tightly confined, or even break.” He explains that the data from the quantum processor reveals the hallmark behaviors of such strings, which have direct analogs to phenomena in high-energy particle physics. The results underscore the potential for quantum computers to facilitate scientific discovery in fundamental physics and beyond.
The research was supported, in part, by the UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number EP/Y036069/1], the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy–EXC–2111–390814868, TRR 360 – 492547816, DFG grants No. KN1254/1-2, KN1254/2-1, DFG FOR 5522 Research Unit (project id 499180199), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 851161 and No. 771537), the European Union (grant agreement No 101169765), as well as the Munich Quantum Valley, which is supported by the Bavarian state government with funds from the Hightech Agenda Bayern Plus. More

Dr. Dong-Soo Han’s research team at the Korea Institute of Science and Technology (KIST) Semiconductor Technology Research Center, in collaboration with the research teams of Prof. Jung-Il Hong at DGIST and Prof. Kyung-Hwan Kim at Yonsei University, has developed a device principle that can utilize “spin loss,” which was previously thought of as a simple loss, as a new power source for magnetic control.
Spintronics is a technology that utilizes the “spin” property of electrons to store and control information, and it is being recognized as a key foundation for next-generation information processing technologies such as ultra-low-power memory, neuromorphic chips, and computational devices for stochastic computation, as it consumes less power and is more non-volatile than conventional semiconductors. This research is significant because it presents a new approach that can significantly improve the efficiency of these spintronics devices.
A team of researchers has identified a new physical phenomenon that allows magnetic materials to spontaneously switch their internal magnetization direction without external stimuli. Magnetic materials are key to the next generation of information processing devices that store information or perform computations by changing the direction of their internal magnetization. For example, if the magnetization direction is upward, it is recognized as ‘1’, and if it is downward, it is recognized as ‘0’, and data can be stored or computed.
Traditionally, to reverse the direction of magnetization, a large current is applied to force the spin of electrons into the magnet. However, this process results in spin loss, where some of the spin does not reach the magnet and is dissipated, which has been considered a major source of power waste and poor efficiency.
Researchers have focused on material design and process improvements to reduce spin loss. But now, the team has found that spin loss actually has the opposite effect, altering magnetization. This means that spin loss induces a spontaneous magnetization switch within the magnetic material, just as the balloon moves as a reaction to the wind being taken out of it.
In their experiments, the team demonstrated the paradox that the greater the spin loss, the less power is required to switch magnetization. As a result, the energy efficiency is up to three times higher than conventional methods, and it can be realized without special materials or complex device structures, making it highly practical and industrially scalable.
In addition, the technology adopts a simple device structure that is compatible with existing semiconductor processes, making it highly feasible for mass production, and it is also advantageous for miniaturization and high integration. This enables applications in various fields such as AI semiconductors, ultra-low power memory, neuromorphic computing, and probability-based computing devices. In particular, the development of high-efficiency computing devices for AI and edge computing is expected to be in full swing.
“Until now, the field of spintronics has focused only on reducing spin losses, but we have presented a new direction by using the losses as energy to induce magnetization switching,” said Dr. Dong-Soo Han, a senior researcher at KIST. “We plan to actively develop ultra-small and low-power AI semiconductor devices, as they can serve as the basis for ultra-low-power computing technologies that are essential in the AI era.”
This research was supported by the Ministry of Science and ICT (Minister Bae Kyung-hoon) through the KIST Institutional Program, the Global TOP Research and Development Project (GTL24041-000), and the Basic Research Project of the National Research Foundation of Korea (2020R1A2C2005932). The results of this research were published in the latest issue of the international journal Nature Communications (IF 15.7, JCR field 7%). More