Computers Math

A new theoretical study provides a framework for understanding nonlocality, a feature that quantum networks must possess to perform operations inaccessible to standard communications technology. By clarifying the concept, researchers determined the conditions necessary to create systems with strong, quantum correlations.
The study, published in Physical Review Letters, adapts techniques from quantum computing theory to create a new classification scheme for quantum nonlocality. This not only allowed the researchers to unify prior studies of the concept into a common framework, but it facilitated a proof that networked quantum systems can only display nonlocality if they possess a particular set of quantum features.
“On the surface, quantum computing and nonlocality in quantum networks are different things, but our study shows that, in certain ways, they are two sides of the same coin,” said Eric Chitambar, a professor of electrical and computer engineering at the University of Illinois Urbana-Champaign and the project lead. “In particular, they require the same fundamental set of quantum operations to deliver effects that cannot be replicated with classical technology.”
Nonlocality is a consequence of entanglement, in which quantum objects experience strong connections even when separated over vast physical distances. When entangled objects are used to perform quantum operations, the results display statistical correlations that cannot be explained by non-quantum means. Such correlations are said to be nonlocal. A quantum network must possess a degree of nonlocality to ensure that it can perform truly quantum functions, but the phenomenon is still poorly understood.
To facilitate study of nonlocality, Chitambar and physics graduate student Amanda Gatto Lamas applied the formalism of quantum resource theory. By treating nonlocality as a “resource” to manage, the researchers’ framework allowed them to view past studies of nonlocality as separate instances of the same concept, just with different restrictions on the resource’s availability. This facilitated the proof of their main result, that nonlocality can only be achieved with a limited set of quantum operations.
“Our result is the quantum network analogue to an important quantum computing result called the Gottesman-Knill theorem,” Gatto Lamas explained. “While Gottesman-Knill clearly defines what a quantum computer must do to surpass a classical one, we show that a quantum network must be constructed with a particular set of operations to do things that a standard communications network cannot.”
Chitambar believes that the framework will not only be useful for developing criteria to assess a quantum network’s quality based on the degree of nonlocality it possesses, but that it can be used to expand the concept of nonlocality.
“Right now, there is a relatively good understanding of the type of nonlocality that can emerge between two parties,” he said. “However, one can imagine for a quantum network consisting of many connected parties that there might be some kind of global property that you can’t reduce to individual pairs on the network. Such a property might depend intimately on the network’s overall structure.” More

A team of researchers has developed new LEDs which emit light simultaneously in two different wavelength ranges, for a simpler and more comprehensive way to monitor the freshness of fruit and vegetables. As the team write in the journal Angewandte Chemie, modifying the LEDs with perovskite materials causes them to emit in both the near-infrared range and the visible range, a significant development in the contact-free monitoring of food.
Perovskite crystals are able to capture and convert light. Being simple to produce and highly efficient, perovskites are already used in solar cells but are also being intensively researched for suitability in other technologies. Angshuman Nag and his team at the Indian Institute of Science Education and Research (IISER) in Pune, India, are now proposing a perovskite application in LED technology that could simplify the quality control of fresh fruit and vegetables.
Without light converters, LEDs would emit light in rather narrow light bands. To cover the whole range of white light produced by the sun, the diodes in “phosphor-converted” (pc) LEDs are coated with luminescent substances. Nag and his team have used a double emission coating with the purpose to produce pc-LEDs that emit both white (“normal”) light and also a strong band in the near-infrared range (NIR).
To make the dual-emission pc-LED, they applied a double perovskite doped with bismuth and chromium. Part of the bismuth component emits warm white light and another part transfers energy to the chromium component, de-exciting it and causing an additional emission in the NIR range, the researchers found out.
NIR is already used in the food industry to examine freshness in fruit and vegetables. Nag and PhD student Sajid Saikia, first author of the paper, explain their idea: “Food contains water, which absorbs the broad near-infrared emission at around 1000 nm. The more water that is present [due to rotting], the greater the absorption of near-infrared radiation, yielding darker contrast in an image taken under near-infrared radiation. This easy, non-invasive imaging process can estimate the water content in different parts of food, assessing its freshness.”
Using these modified pc-LEDs to examine apples or strawberries, the team observed dark spots that were not visible in standard camera images. Illuminating the food with both white and NIR light revealed normal coloring that could be seen by the naked eye, as well as those parts which were starting to rot, but not yet visibly so.
Saikia and Nag envision a compact device for simultaneous visual and NIR food inspection, although the two detectors, one for visible light and one for NIR light, could make such an instrument costly for common applications. On the other hand, the researchers emphasize that the pc LEDs are easy to produce without any chemical waste or solvents and short-term costs could be more than recovered by the long service life and scalability of this novel dual-emitting pc-LED device. More

A team of scientists has devised a system that replicates the movement of naturally occurring phenomena, such as hurricanes and algae, using laser beams and the spinning of microscopic rotors.
The breakthrough, reported in the journal Nature Communications, reveals new ways that living matter can be reproduced on a cellular scale.
“Living organisms are made of materials that actively pump energy through their molecules, which produce a range of movements on a larger cellular scale,” explains Matan Yah Ben Zion, a doctoral student in New York University’s Department of Physics at the time of the work and one of the paper’s authors. “By engineering cellular-scale machines from the ground up, our work can offer new insights into the complexity of the natural world.”
The research centers on vortical flows, which appear in both biological and meteorological systems, such as algae or hurricanes. Specifically, particles move into orbital motion in the flow generated by their own rotation, resulting in a range of complex interactions.
To better understand these dynamics, the paper’s authors, who also included Alvin Modin, an NYU undergraduate at the time of the study and now a doctoral student at Johns Hopkins University, and Paul Chaikin, an NYU physics professor, sought to replicate them at their most basic level. To do so, they created tiny micro-rotors — about 1/10th the width of a strand of human hair — to move micro-particles using a laser beam (Chaikin and his colleagues devised this process in a previous work).
The researchers found that the rotating particles mutually affected each other into orbital motion, with striking similarities to dynamics observed by other scientists in “dancing” algae — algae groupings that move in concert with each other.
In addition, the NYU team found that the spins of the particles reciprocate as the particles orbit.
“The spins of the synthetic particles reciprocate in the same fashion as that observed in algae — in contrast to previous work with artificial micro-rotors,” explains Ben Zion, now a researcher at Tel Aviv University. “So we were able to reproduce synthetically — and on the micron scale — an effect that is seen in living systems.”
“Collectively, these findings suggest that the dance of algae can be reproduced in a synthetic system, better establishing our understanding of living matter,” he adds.
The research was supported by grants from the Department of Energy (DE-SC0007991, SC0020976). More

Artificial intelligence (AI) is all the rage lately in the public eye. How AI is being incorporated to the advantage of our everyday life despite its rapid development, however, remains an elusive topic that deserves the attention of many scientists. While in theory AI can replace, or even displace, human beings from their positions, the challenge remains on how different industries and institutions can take advantage of this technological advancement and not drown in it.
Recently, a team of researchers at the Hong Kong University of Science and Technology (HKUST) conducted an ambitious study of AI applications on the education front, examining how AI could enhance grading while observing human participants’ behavior in the presence of a computerized companion. They found that teachers were generally receptive to AI’s input — until both sides came to an argument on who should reign supreme. This very much resembles how human beings interact with one another when a new member forays into existing territory.
The research was conducted by HKUST Department of Computer Science and Engineering Ph.D. candidate Chengbo Zheng and four of his teammates under the supervision of Associate Professor Prof. Xiaojuan MA. They developed an AI group member named AESER (Automated Essay ScorER) and separated twenty English teachers into ten groups to investigate the impact of AESER in a group discussion setting, where the AI would contribute in opinion deliberation, asking and answering questions and even voting for the final decision. In this study, designed akin to the controlled “Wizard of Oz” research method, a deep learning model and a human researcher would form joint input to AESER, which would then exchange views and conduct discussions with other participants in an online meeting room.
While the team expected AESER to promote objectivity and provide novel perspectives that would otherwise be overlooked, potential challenges were soon revealed. First, there was the risk of conformity, where the engagement of AI would soon create a majority to thwart discussions. Second, views provided by AESER were found to be rigid and even stubborn, which frustrated the participants when they found that an argument could never be “won.” Many also did not think AI’s input should be given equal weight and are more fit to play the role of an assistant to actual human work.
“At this stage, AI is deemed somewhat ‘stubborn’ by human collaborators, for good and bad,” noted Prof Ma. “On the one hand, AI is stubborn so it does not fear to express its opinions frankly and openly. However, human collaborators feel disengaged when they could not meaningfully persuade AI to change its view. Humans varying attitudes towards AI. Some consider it to be a single intelligent entity while others regard AI as the voice of collective intelligence that emerges from big data. Discussions about issues such as authority and bias thus arise.”
The immediate next step for the team involves expanding its scope to gathermore quantitative data, which will provide more measurable and precise insights into how AI impacts group decision-making. They are also looking to explore large language models (LLMs) such as ChatGPT into the study, which could potentially bring new insights and perspectives to group discussions. More

A University of Minnesota Twin Cities team has, for the first time, synthesized a thin film of a unique topological semimetal material that has the potential to generate more computing power and memory storage while using significantly less energy. The researchers were also able to closely study the material, leading to some important findings about the physics behind its unique properties.
The study is published in Nature Communications, a peer-reviewed scientific journal that covers the natural sciences and engineering.
As evidenced by the United States’ recent CHIPS and Science Act, there is a growing need to increase semiconductor manufacturing and support research that goes into developing the materials that power electronic devices everywhere. While traditional semiconductors are the technology behind most of today’s computer chips, scientists and engineers are always looking for new materials that can generate more power with less energy to make electronics better, smaller, and more efficient.
One such candidate for these new and improved computer chips is a class of quantum materials called topological semimetals. The electrons in these materials behave in different ways, giving the materials unique properties that typical insulators and metals used in electronic devices do not have. For this reason, they are being explored for use in spintronic devices, an alternative to traditional semiconductor devices that leverage the spin of electrons rather than the electrical charge to store data and process information.
In this new study, an interdisciplinary team of University of Minnesota researchers were able to successfully synthesize such a material as a thin film — and prove that it has the potential for high performance with low energy consumption.
“This research shows for the first time that you can transition from a weak topological insulator to a topological semimetal using a magnetic doping strategy,” said Jian-Ping Wang, a senior author of the paper and a Distinguished McKnight University Professor and Robert F. Hartmann Chair in the University of Minnesota Department of Electrical and Computer Engineering. “We’re looking for ways to extend the lifetimes for our electrical devices and at the same time lower the energy consumption, and we’re trying to do that in non-traditional, out-of-the-box ways.”
Researchers have been working on topological materials for years, but the University of Minnesota team is the first to use a patented, industry-compatible sputtering process to create this semimetal in a thin film format. Because their process is industry compatible, Wang said, the technology can be more easily adopted and used for manufacturing real-world devices.

“Every day in our lives, we use electronic devices, from our cell phones to dishwashers to microwaves. They all use chips. Everything consumes energy,” said Andre Mkhoyan, a senior author of the paper and Ray D. and Mary T. Johnson Chair and Professor in the University of Minnesota Department of Chemical Engineering and Materials Science. “The question is, how do we minimize that energy consumption? This research is a step in that direction. We are coming up with a new class of materials with similar or often better performance, but using much less energy.”
Because the researchers fabricated such a high-quality material, they were also able to closely analyze its properties and what makes it so unique.
“One of the main contributions of this work from a physics point of view is that we were able to study some of this material’s most fundamental properties,” said Tony Low, a senior author of the paper and the Paul Palmberg Associate Professor in the University of Minnesota Department of Electrical and Computer Engineering. “Normally, when you apply a magnetic field, the longitudinal resistance of a material will increase, but in this particular topological material, we have predicted that it would decrease. We were able to corroborate our theory to the measured transport data and confirm that there is indeed a negative resistance.”
Low, Mkhoyan, and Wang have been working together for more than a decade on topological materials for next generation electronic devices and systems — this research wouldn’t have been possible without combining their respective expertise in theory and computation, material growth and characterization, and device fabrication.
“It not only takes an inspiring vision but also great patience across the four disciplines and a dedicated group of team members to work on such an important but challenging topic, which will potentially enable the transition of the technology from lab to industry,” Wang said.
In addition to Low, Mkhoyan, and Wang, the research team included University of Minnesota Department of Electrical and Computer Engineering researchers Delin Zhang, Wei Jiang, Onri Benally, Zach Cresswell, Yihong Fan, Yang Lv, and Przemyslaw Swatek; Department of Chemical Engineering and Materials Science researcher Hwanhui Yun; Department of Physics and Astronomy researcher Thomas Peterson; and University of Minnesota Characterization Facility researchers Guichuan Yu and Javier Barriocanal.
This research is supported by SMART, one of seven centers of nCORE, a Semiconductor Research Corporation program, sponsored by National Institute of Standards and Technology (NIST). T.P. and D.Z. were partly supported by ASCENT, one of six centers of JUMP, a Semiconductor Research Corporation program that is sponsored by MARCO and DARPA. This work was partially supported by the University of Minnesota’s Materials Research Science and Engineering Center (MRSEC) program under award number DMR-2011401 (Seed). Parts of this work were carried out in the Characterization Facility of the University of Minnesota Twin Cities, which receives partial support from the National Science Foundation through the MRSEC (Award NumberDMR-2011401). Portions of this work were conducted in the Minnesota Nano Center, which is supported by the NSF Nano Coordinated Infrastructure Network (NNCI) under Award Number ECCS-2025124. More

The future of electronics will be based on novel kinds of materials. Sometimes, however, the naturally occurring topology of atoms makes it difficult for new physical effects to be created. To tackle this problem, researchers at the University of Zurich have now successfully designed superconductors one atom at a time, creating new states of matter.
What will the computer of the future look like? How will it work? The search for answers to these questions is a major driver of basic physical research. There are several possible scenarios, ranging from the further development of classical electronics to neuromorphic computing and quantum computers. The common element in all these approaches is that they are based on novel physical effects, some of which have so far only been predicted in theory. Researchers go to great lengths and use state-of-the-art equipment in their quest for new quantum materials that will enable them to create such effects. But what if there are no suitable materials that occur naturally?
Novel approach to superconductivity
In a recent study published in Nature Physics, the research group of UZH Professor Titus Neupert, working closely together with physicists at the Max Planck Institute of Microstructure Physics in Halle (Germany), presented a possible solution. The researchers made the required materials themselves — one atom at a time. They are focusing on novel types of superconductors, which are particularly interesting because they offer zero electrical resistance at low temperatures. Sometimes referred to as “ideal diamagnets,” superconductors are used in many quantum computers due to their extraordinary interactions with magnetic fields. Theoretical physicists have spent years researching and predicting various superconducting states. “However, only a small number have so far been conclusively demonstrated in materials,” says Professor Neupert.
Two new types of superconductivity
In their exciting collaboration, the UZH researchers predicted in theory how the atoms should be arranged to create a new superconductive phase, and the team in Germany then conducted experiments to implement the relevant topology. Using a scanning tunneling microscope, they moved and deposited the atoms in the right place with atomic precision. The same method was also used to measure the system’s magnetic and superconductive properties. By depositing chromium atoms on the surface of superconducting niobium, the researchers were able to create two new types of superconductivity. Similar methods had previously been used to manipulate metal atoms and molecules, but until now it has never been possible to make two-dimensional superconductors with this approach.
The results not only confirm the physicists’ theoretical predictions, but also give them reason to speculate about what other new states of matter might be created in this way, and how they could be used in the quantum computers of the future. More

Researchers have demonstrated a continuous roller printing process that can pick up and transfer over 75,000 micrometer-scale semiconductor devices in a single roll with very high accuracy. The new method paves the way to creating large-scale arrays of optical components and could be used to rapidly manufacture micro-LED displays.
Micro-LED display technology is of great interest because it can accomplish highly accurate color rendering with high speed and resolution while using little power. These displays can be applied in a wide range of formats including smartphone screens, virtual and augmented reality devices and large displays several meters across. For larger micro-LED displays, in particular, the challenges of integrating millions of tiny LEDs — which are sometimes smaller than a grain of fine sand — onto an electronic control backplane are enormous.
“Transferring micrometer-scale semiconductor devices from their native substrate to a variety of receiving platforms is a challenge being tackled internationally by both academic research groups and industries,” said research team leader Eleni Margariti from the University of Strathclyde in the UK. “Our roller-based printing process offers a way to achieve this in a scalable manner while meeting the demanding accuracy necessary for this application.”
In the journal Optical Materials Express, the researchers report that their new roller technology can match the designed device layout with an accuracy of less than 1 micron. The setup is also inexpensive and simple enough to be constructed in locations with limited resources.
“This printing process could also be used for other types of devices including silicon and printed electronics such as transistors, sensors and antennas for flexible and wearable electronics, smart packaging and radio-frequency identification tags,” said Margariti, who developed the new printing process. “It could also be useful for making photovoltaics and for biomedical applications such as drug delivery systems, biosensors and tissue engineering.”
Large-scale device transfer
Today’s semiconductor devices are typically manufactured on wafers using growth techniques that deposit exquisitely detailed, multi-layer semiconductor thin films onto semiconductor substrates. Compatibility issues between these thin film structures and the types of substrates suitable for this deposition constrain the ways in which the devices can be used.

“We wanted to improve the transfer of large numbers of semiconductor devices from one substrate to another to improve the performance and scaling of electronic systems used in applications such as displays and on-chip photonics, where the aim is to combine various materials that manipulate light on a very small scale,” said Margariti. “To be used for large-scale manufacturing, it is crucial to use methods that can transfer these devices efficiently, accurately and with minimal errors.”
The new approach starts with an array of tiny devices that are loosely attached to their growth substrate. The surface of a cylinder containing a slightly sticky silicone polymer film is then rolled over the suspended array of devices, allowing adhesive forces between the silicone and semiconductor to detach the devices from their growth substrate and array them on the cylinder drum. Because the printing process is continuous it can be used to simultaneously print numerous devices, which makes it highly efficient for large-scale production.
Highly accurate printing
“By carefully selecting the properties of the silicone and receiving substrate surface and the speed and mechanics of the rolling process, the devices can be successfully rolled over and released onto the receiver substrate while preserving the spatially arrayed format they had on the original substrate,” explained Margariti. “We also developed a custom analysis method that scans the printed sample for defects and provides the printing yield and positioning accuracy in just minutes.”
The researchers tested the new approach with gallium nitride on silicon (GaN/Si) semiconductor structures. GaN is the dominant semiconductor technology used for micro-LED displays, and using silicon substrates facilitated the preparation of the devices as suspended structures that could be picked up by the roller. They were able to transfer more than 99% of the devices in an array of over 76,000 individual elements with a spatial precision below a micron with no significant rotational errors.
Next, the researchers are working to further improve the accuracy of the printing process while also scaling up the number of devices that can be transferred at once. They also plan to test the method’s ability to combine different types of devices onto the same receiving platform and determine if it can be used to print to specific locations of the receiving platform. More

Many scholars, analysts, and other observers have suggested that resistance to innovation is an Achilles’ heel of authoritarian regimes. Such governments can fail to keep up with technological changes that help their opponents; they may also, by stifling rights, inhibit innovative economic activity and weaken the long-term condition of the country.
But a new study co-led by an MIT professor suggests something quite different. In China, the research finds, the government has increasingly deployed AI-driven facial-recognition technology to suppress dissent; has been successful at limiting protest; and in the process, has spurred the development of better AI-based facial-recognition tools and other forms of software.
“What we found is that in regions of China where there is more unrest, that leads to greater government procurement of facial-recognition AI, subsequently, by local government units such as municipal police departments,” says MIT economist Martin Beraja, who is co-author of a new paper detailing the findings.
What follows, as the paper notes, is that “AI innovation entrenches the regime, and the regime’s investment in AI for political control stimulates further frontier innovation.”
The scholars call this state of affairs an “AI-tocracy,” describing the connected cycle in which increased deployment of the AI-driven technology quells dissent while also boosting the country’s innovation capacity.
The open-access paper, also called “AI-tocracy,” appears in the August issue of the Quarterly Journal of Economics. An abstract of the uncorrected proof was first posted online in March. The co-authors are Beraja, who is the Pentti Kouri Career Development Associate Professor of Economics at MIT; Andrew Kao, a doctoral candidate in economics at Harvard University; David Yang, a professor of economics at Harvard; and Noam Yuchtman, a professor of management at the London School of Economics.

To conduct the study, the scholars drew on multiple kinds of evidence spanning much of the last decade. To catalogue instances of political unrest in China, they used data from the Global Database of Events, Language, and Tone (GDELT) Project, which records news feeds globally. The team turned up 9,267 incidents of unrest between 2014 and 2020.
The researchers then examined records of almost 3 million procurementcontracts issued by the Chinese government between 2013 and 2019, from a database maintained by China’s Ministry of Finance. They found that local governments’ procurement of facial-recognition AI services and complementary public security tools — high-resolution video cameras — jumped significantly in the quarter following an episode of public unrest in that area.
Given that Chinese government officials were clearly responding to public dissent activities by ramping up on facial-recognition technology, the researchers then examined a follow-up question: Did this approach work to suppress dissent?
The scholars believe that it did, although as they note in the paper, they “cannot directly estimate the effect” of the technology on political unrest. But as one way of getting at that question, they studied the relationship between weather and political unrest in different areas of China. Certain weather conditions are conducive to political unrest. But in prefectures in China that had already invested heavily in facial-recognition technology, such weather conditions are less conducive to unrest compared to prefectures that had not made the same investments.
In so doing, the researchers also accounted for issues such as whether or not greater relative wealth levels in some areas might have produced larger investments in AI-driven technologies regardless of protest patterns. However, the scholars still reached the same conclusion: Facial-recognition technology was being deployed in response to past protests, and then reducing further protest levels.

“It suggests that the technology is effective in chilling unrest,” Beraja says.
Finally, the research team studied the effects of increased AI demand on China’s technology sector and found the government’s greater use of facial-recognition tools appears to be driving the country’s tech sector forward. For instance, firms that are granted procurement contracts for facial-recognition technologies subsequently produce about 49 percent more software products in the two years after gaining the government contract than they had beforehand.
“We examine if this leads to greater innovation by facial-recognition AI firms, and indeed it does,” Beraja says.
Such data — from China’s Ministry of Industry and Information Technology — also indicates that AI-driven tools are not necessarily “crowding out” other kinds of high-tech innovation.
Adding it all up, the case of China indicates how autocratic governments can potentially reach a near-equilibrium state in which their political power is enhanced, rather than upended, when they harness technological advances.
“In this age of AI, when the technologies not only generate growth but are also technologies of repression, they can be very useful” to authoritarian regimes, Beraja says.
The finding also bears on larger questions about forms of government and economic growth. A significant body of scholarly research shows that rights-granting democratic institutions do generate greater economic growth over time, in part by creating better conditions for technological innovation. Beraja notes that the current study does not contradict those earlier findings, but in examining the effects of AI in use, it does identify one avenue through which authoritarian governments can generate more growth than they otherwise would have.
“This may lead to cases where more autocratic institutions develop side by side with growth,” Beraja adds.
Other experts in the societal applications of AI say the paper makes a valuable contribution to the field.
“This is an excellent and important paper that improves our understanding of the interaction between technology, economic success, and political power,” says Avi Goldfarb, the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management at the University of Toronto. “The paper documents a positive feedback loop between the use of AI facial-recognition technology to monitor suppress local unrest in China and the development and training of AI models. This paper is pioneering research in AI and political economy. As AI diffuses, I expect this research area to grow in importance.”
For their part, the scholars are continuing to work on related aspects of this issue. One forthcoming paper of theirs examines the extent to which China is exporting advanced facial-recognition technologies around the world — highlighting a mechanism through which government repression could grow globally.
Support for the research was provided in part by the U.S. National Science Foundation Graduate Research Fellowship Program; the Harvard Data Science Initiative; and the British Academy’s Global Professorships program. More