Aurelia Butler

Be it magnets or superconductors: materials are known for their various properties. However, these properties may change spontaneously under extreme conditions. Researchers at the Technische Universität Dresden (TUD) and the Technische Universität München (TUM) have discovered an entirely new type of such phase transitions. They display the phenomenon of quantum entanglement involving many atoms, which previously has only been observed in the realm of few atoms. The results were recently published in the scientific journal Nature.
In physics, Schroedinger’s cat is an allegory for two of the most awe-inspiring effects of quantum mechanics: entanglement and superposition. Researchers from Dresden and Munich have now observed these behaviors on a much larger scale than that of the smallest of particles. Until now, materials that display properties like, e.g., magnetism have been known to have so-called domains — islands in which the materials properties are homogeneously either of one or a different kind (imagine them being either black or white, for example). Looking at lithium holmium fluoride (LiHoF4), the physicists have now discovered a completely new phase transition, at which the domains surprisingly exhibit quantum mechanical features, resulting in their properties becoming entangled (being black and white at the same time). “Our quantum cat now has a new fur because we’ve discovered a new quantum phase transition in LiHoF4 which has not previously been known to exist,” comments Matthias Vojta, Chair of Theoretical Solid State Physics at TUD.
Phase transitions and entanglement
We can easily observe the spontaneously changing properties of a substance if we look at water: at 100 degrees Celsius it evaporates into a gas, at zero degrees Celsius it freezes into ice. In both cases, these new states of matter form as a consequence of a phase transition where the water molecules rearrange themselves, thus changing the characteristics of the matter. Properties like magnetism or superconductivity emerge as a result of electrons undergoing phase transitions in crystals. For phase transitions at temperatures approaching the absolute zero at -273.15 degrees Celsius, quantum mechanical effects such as entanglement come into play, and one speaks of quantum phase transitions. “Even though there are more than 30 years of extensive research dedicated to phase transitions in quantum materials, we had previously assumed that the phenomenon of entanglement played a role only on a microscopic scale, where it involves only a few atoms at a time,” explains Christian Pfleiderer, Professor of Topology of Correlated Systems at the TUM.
Quantum entanglement is one of the most astonishing phenomena of physics, where the entangled quantum particles exist in a shared superposition state that allows for usually mutually exclusive properties (e.g., black and white) to occur simultaneously. As a rule, the laws of quantum mechanics only apply to microscopic particles. The research teams from Munich and Dresden have now succeeded in observing effects of quantum entanglement on a much larger scale, that of thousands of atoms. For this, they have chosen to work with the well-known compound LiHoF4.
Spherical samples enable precision measurements
At very low temperatures, LiHoF4 acts as a ferromagnet where all magnetic moments spontaneously point in the same direction. If you then apply a magnetic field exactly vertically to the preferred magnetic direction, the magnetic moments will change direction, which is known as fluctuations. The higher the magnetic field strength, the stronger these fluctuations become, until, eventually, the ferromagnetism disappears completely at a quantum phase transition. This leads to the entanglement of neighboring magnetic moments. “If you hold up a LiHoF4 sample to a very strong magnet, it suddenly ceases to be spontaneously magnetic. This has been known for 25 years,” summarizes Vojta.
What is new is what happens when you change the direction of the magnetic field. “We discovered that the quantum phase transition continues to occur, whereas it had previously been believed that even the smallest tilt of the magnetic field would immediately suppress it,” explains Pfleiderer. Under these conditions, however, it is not individual magnetic moments but rather extensive magnetic areas, so-called ferromagnetic domains, that undergo these quantum phase transitions. The domains constitute entire islands of magnetic moments pointing in the same direction. “We have used spherical samples for our precision measurements. That is what enabled us to precisely study the behavior upon small changes in the direction of the magnetic field,” adds Andreas Wendl, who conducted the experiments as part of his doctoral dissertation.
From fundamental physics to applications
“We have discovered an entirely new type of quantum phase transitions where entanglement takes place on the scale of many thousands of atoms instead of just in the microcosm of only a few,” explains Vojta. “If you imagine the magnetic domains as a black-and-white pattern, the new phase transition leads to either the white or the black areas becoming infinitesimally small, i.e., creating a quantum pattern, bevor dissolving completely.” A newly developed theoretical model successfully explains the data obtained from the experiments. “For our analysis, we generalized existing microscopic models and also took into account the feedback of the large ferromagnetic domains to the microscopic properties,” elaborates Heike Eisenlohr, who performed the calculations as part of her PhD thesis.
The discovery of the new quantum phase transitions is important as a foundation and general frame of reference for the research of quantum phenomena in materials, as well as for new applications. “Quantum entanglement is applied and used in technologies like quantum sensors and quantum computers, amongst other things,” says Vojta. Pfleiderer adds: “Our work is in the area of fundamental research, which, however, can have a direct impact on the development of practical applications, if you use the materials properties in a controlled way.”
The research has been financially supported by the Excellence Strategy of the German Federal and State Governments within the Würzburg-Dresden Cluster of Excellence Complexity and Topology in Quantum Matter (ct.qmat) and the Cluster of Excellence Munich Center for Quantum Science and Technology (MCQST). In addition, the work has been supported by the European Research Council (ERC) via the Advanced Grant ExQuiSid and by the Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Centers (SFB) 1143 und TRR80. More

Artificial intelligence (AI) can be trained to recognise whether a tissue image contains a tumour. However, exactly how it makes its decision has remained a mystery until now. A team from the Research Center for Protein Diagnostics (PRODI) at Ruhr-Universität Bochum is developing a new approach that will render an AI’s decision transparent and thus trustworthy. The researchers led by Professor Axel Mosig describe the approach in the journal Medical Image Analysis, published online on 24 August 2022.
For the study, bioinformatics scientist Axel Mosig cooperated with Professor Andrea Tannapfel, head of the Institute of Pathology, oncologist Professor Anke Reinacher-Schick from the Ruhr-Universität’s St. Josef Hospital, and biophysicist and PRODI founding director Professor Klaus Gerwert. The group developed a neural network, i.e. an AI, that can classify whether a tissue sample contains tumour or not. To this end, they fed the AI a large number of microscopic tissue images, some of which contained tumours, while others were tumour-free.
“Neural networks are initially a black box: it’s unclear which identifying features a network learns from the training data,” explains Axel Mosig. Unlike human experts, they lack the ability to explain their decisions. “However, for medical applications in particular, it’s important that the AI is capable of explanation and thus trustworthy,” adds bioinformatics scientist David Schuhmacher, who collaborated on the study.
AI is based on falsifiable hypotheses
The Bochum team’s explainable AI is therefore based on the only kind of meaningful statements known to science: on falsifiable hypotheses. If a hypothesis is false, this fact must be demonstrable through an experiment. Artificial intelligence usually follows the principle of inductive reasoning: using concrete observations, i.e. the training data, the AI creates a general model on the basis of which it evaluates all further observations.
The underlying problem had been described by philosopher David Hume 250 years ago and can be easily illustrated: No matter how many white swans we observe, we could never conclude from this data that all swans are white and that no black swans exist whatsoever. Science therefore makes use of so-called deductive logic. In this approach, a general hypothesis is the starting point. For example, the hypothesis that all swans are white is falsified when a black swan is spotted.
Activation map shows where the tumour is detected
“At first glance, inductive AI and the deductive scientific method seem almost incompatible,” says Stephanie Schörner, a physicist who likewise contributed to the study. But the researchers found a way. Their novel neural network not only provides a classification of whether a tissue sample contains a tumour or is tumour-free, it also generates an activation map of the microscopic tissue image.
The activation map is based on a falsifiable hypothesis, namely that the activation derived from the neural network corresponds exactly to the tumour regions in the sample. Site-specific molecular methods can be used to test this hypothesis.
“Thanks to the interdisciplinary structures at PRODI, we have the best prerequisites for incorporating the hypothesis-based approach into the development of trustworthy biomarker AI in the future, for example to be able to distinguish between certain therapy-relevant tumour subtypes,” concludes Axel Mosig.
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The National Institute of Information and Communications Technology, Keio University, Tokyo University of Science, and The University of Tokyo, succeeded for the first time in developing a method for systematically finding the optimal quantum operation sequence for a quantum computer.
In order for a quantum computer to perform a task, we need to write a sequence of quantum operations. Until now, computer operators have written their own quantum operation sequences based on existing methods (recipes). What we have developed this time is a systematic method that applies optimal control theory (GRAPE algorithm) to identify the theoretically optimal sequence from among all conceivable quantum operation sequences.
This method is expected to become a useful tool for medium-scale quantum computers and is expected to contribute to improving the performance of quantum computers and reducing environmental impact in the near future.
This result was published in the American scientific journal Physical Review A on August 23, 2022.
Quantum computers, which are currently under development, are expected to have a major impact on society. Their benefits include reducing the environmental burden by reducing energy consumption, finding new chemical substances for medical use, accelerating the search for materials for a cleaner environment, etc.
One of the big problems for quantum computers is that the quantum state is very sensitive to noise, so it is difficult to maintain it stably for a long time (maintaining a coherent quantum state). In order to obtain the best performance, it is necessary to complete the operations within the time that the coherent quantum state is maintained. There was a need for a method to systematically identify the optimal sequences.
The research team has developed a systematic method to identify the optimal quantum operation sequence.
When a computer stores and processes information, all information is converted to a string of bits with values of 0 or 1. A quantum operation sequence is a computer program written in a human-readable language that is converted so that it can be processed by a quantum computer. The quantum operation sequence consists of 1-qubit operations and 2-qubit operations. The best sequence is the one with the fewest operations and shows the best performance (the number of red squares and green vertical lines is the smallest).
The new method analyzes all possible sequences of elementary quantum operations using a computational algorithm called GRAPE, a numerical optimal control theory algorithm. Specifically, we create a table of quantum operation sequences and the performance index (fidelity F) for each sequence, ranging from thousands to millions, depending on the number of qubits and the number of operations under investigation. The optimal quantum operation sequence is systematically identified based on the accumulated data.
It is also possible for the new method to analyze the complete list of all quantum operation sequences and evaluate conventional recipes. As such, it can provide a valuable tool for establishing benchmarks for past and future research on the performance of few-qubit quantum algorithms.
The systematic method to find the optimal quantum operation sequence for quantum computers is expected to become a useful tool for medium-scale quantum computers. In the near future, it is expected to improve the performance of quantum computers  and contribute to reducing the burden on the environment.
We also found that there are many optimal sequences of quantum operations that are excellent. This means that a probabilistic approach could extend the applicability of this new method to larger tasks. Approaches based on analyzing large datasets suggest the possibility of integrating machine learning with our new method to further enhance the predictive power. In the future, the research team will apply the results obtained this time to the optimization of tasks obtained from actual quantum algorithms. More

Researchers at Linköping University have used computer simulations to show that stable aromatic molecules can become reactive after absorbing light. The results, published in the Journal of Organic Chemistry, may have long-term applications in such areas as the storage of solar energy, pharmacology, and molecular machines.
“Everyone knows that petrol smells nice. This is because it contains the aromatic molecule benzene. And aromatic molecules don’t just smell nice: they have many useful chemical properties. Our discovery means that we can add more properties,” says Bo Durbeej, professor of computational physics at Linköping University.
In normal organic chemistry, heat can be used to start reactions. However, an aromatic molecule is a stable hydrocarbon, and it is difficult to initiate reactions between such molecules and others simply by heating. This is because the molecule is already in an optimal energy state. In contrast, a reaction in which an aromatic molecule is formed takes place extremely readily.
Researchers at Linköping University have now used computer simulations to show that it is possible to activate aromatic molecules using light. Reactions of this type are known as photochemical reactions.
“It is possible to add more energy using light than using heat. In this case, light can help an aromatic molecule to become antiaromatic, and thus highly reactive. This is a new way to control photochemical reactions using the aromaticity of the molecules,” says Bo Durbeej.
The result was important enough to be highlighted on the cover of the Journal of Organic Chemistry when it was published. In the long term, it has possible applications in many areas. Bo Durbeej’s research group focuses on applications in the storage of solar energy, but he sees potential also in molecular machines, molecular synthesis, and photopharmacology. In the latter application, it may be possible to use light to selectively activate drugs with aromatic groups at a location in the body where the pharmacological effect is wanted.
“In some cases, it’s not possible to supply heat without harming surrounding structures, such as body tissue. It should, however, be possible to supply light,” says Bo Durbeej.
The researchers tested the hypothesis that it was the loss of aromaticity that led to the increased reactivity by examining the opposite relationship in the simulations. In this case, they started with an antiaromatic unstable molecule and simulated it being subject to light irradiation. This led to the formation of an aromatic compound, and the researchers saw, as expected, that the reactivity was lost.
“Our discovery extends the concept of ‘aromaticity’, and we have shown that we can use this concept in organic photochemistry,” says Bo Durbeej.
The study has been funded by the Olle Engkvist Foundation, the Swedish Research Council, ÅForsk, and the Carl Trygger Foundation. The computations were carried out at the National Supercomputer Centre at Linköping University with support from the Swedish National Infrastructure for Computing (SNIC).
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Materials provided by Linköping University. Original written by Anders Törneholm. Note: Content may be edited for style and length. More

A market-led approach could be key to guiding policy, research and business decisions about future climate risks, a new study outlines.
Published in the journal Nature Climate Change, the paper from academics at the Universities of Lancaster and Exeter details how expert ‘prediction markets’ could improve the climate-risk forecasts that guide key business and regulatory decisions.
Organisations now appreciate that they have to consider climate risks within their strategic plans — whether that relates to physical risks to buildings and sites, or risks associated with transitioning to achieve net zero.
However, the forward-looking information needed to inform these strategic decisions is limited, the researchers say.
Dr Kim Kaivanto, a co-author from Lancaster University’s Department of Economics, said: “The institutional arrangements under which climate-risk information is currently provided mirrors the incentive problems and conflicts of interest that prevailed in the credit-rating industry prior to the 2007/8 financial crisis.
“In order to make sense of emissions scenarios and to support planning and decision-making, organisations have a pressing need for this type of forward-looking expert risk information. More

Robots can be better at detecting mental wellbeing issues in children than parent-reported or self-reported testing, a new study suggests.
A team of roboticists, computer scientists and psychiatrists from the University of Cambridge carried out a study with 28 children between the ages of eight and 13, and had a child-sized humanoid robot administer a series of standard psychological questionnaires to assess the mental wellbeing of each participant.
The children were willing to confide in the robot, in some cases sharing information with the robot that they had not yet shared via the standard assessment method of online or in-person questionnaires. This is the first time that robots have been used to assess mental wellbeing in children.
The researchers say that robots could be a useful addition to traditional methods of mental health assessment, although they are not intended to be a substitute for professional mental health support. The results will be presented today (1 September) at the 31st IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) in Naples, Italy.
During the COVID-19 pandemic, home schooling, financial pressures, and isolation from peers and friends impacted the mental health of many children. Even before the pandemic however, anxiety and depression among children in the UK has been on the rise, but the resources and support to address mental wellbeing are severely limited.
Professor Hatice Gunes, who leads the Affective Intelligence and Robotics Laboratory in Cambridge’s Department of Computer Science and Technology, has been studying how socially-assistive robots (SARs) can be used as mental wellbeing ‘coaches’ for adults, but in recent years has also been studying how they may be beneficial to children. More

As public health officials around the world contend with the latest surge of the COVID-19 pandemic, researchers at Drexel University have created a computer model that could help them be better prepared for the next one. Using machine learning algorithms, trained to identify correlations between changes in the genetic sequence of the COVID-19 virus and upticks in transmission, hospitalizations and deaths, the model can provide an early warning about the severity of new variants.
More than two years into the pandemic, scientists and public health officials are doing their best to predict how mutations of the SARS-CoV-2 virus are likely to make it more transmissible, evasive to the immune system and likely to cause severe infections. But collecting and analyzing the genetic data to identify new variants — and linking it to the specific patients who have been sickened by it — is still an arduous process.
Because of this, most public health projections about new “variants of concern” — as the World Health Organization categorizes them — are based on surveillance testing and observation of the regions where they are already spreading.
“The speed with which new variants, like Omicron have made their way around the globe means that by the time public health officials have a good handle on how vulnerable their population might be, the virus has already arrived,” said Bahrad A. Sokhansanj, PhD, an assistant research professor in Drexel’s College of Engineering who led development of the computer model. “We’re trying to give them an early warning system — like advanced weather modeling for meteorologists — so they can quickly predict how dangerous a new variant is likely to be — and prepare accordingly.”
The Drexel model, which was recently published in the journal Computers in Biology and Medicine, is driven by a targeted analysis of the genetic sequence of the virus’s spike protein — the part of the virus that allows it to evade the immune system and infect healthy cells, it is also the part known to have mutated most frequently throughout the pandemic — combined with a mixed effects machine learning analysis of factors such as age, sex and geographic location of COVID patients.
Learning to Find Patterns
The research team used a newly developed machine learning algorithm, called GPBoost, based on methods commonly used by large companies to analyze sales data. Via a textual analysis, the program can quickly home in on the areas of the genetic sequence that are most likely to be linked to changes in the severity of the variant. More

Granular matter is all around us. Examples include sand, rice, nuts, coffee and even snow. These materials are made of solid particles that are large enough not to experience thermal fluctuations. Instead, their state is determined by mechanical influences: shaking produces “granular gases” whilst by compression one gets “granular solids.” An unusual feature of such solids is that forces within the material concentrate along essentially linear paths called force chains whose shape resembles that of lightning. Apart from granular solids, other complex solids such as dense emulsions, foams and even groups of cells can exhibit these force chains. Researchers led by the University of Göttingen used machine learning and computer simulations to predict the position of force chains. The results were published in Nature Communications.
The formation of force chains is highly sensitive to the way the individual grains interact. This makes it very difficult to predict where force chains will form. Combining computer simulations with tools from artificial intelligence, researchers at the Institute for Theoretical Physics, University of Göttingen, and at Ghent University tackled this challenge by developing a novel tool for predicting the formation of force chains in both frictionless and frictional granular matter. The approach uses a machine learning method known as a graph neural network (GNN). The researchers have demonstrated that GNNs can be trained in a supervised approach to predict the position of force chains that arise while deforming a granular system, given an undeformed static structure.
“Understanding force chains is crucial in describing the mechanical and transport properties of granular solids and this applies in a wide range of circumstances — for example how sound propagates or how sand or a pack of coffee grains respond to mechanical deformation,” explains Dr Rituparno Mandal, Institute for Theoretical Physics, University of Göttingen. Mandal adds, “A recent study even suggests that living creatures such as ants exploit the effects of force chain networks when removing grains of soil for efficient tunnel excavation.”
“We experimented with different machine learning-based tools and realised that a trained GNN can generalize remarkably well from training data, allowing it to predict force chains in new undeformed samples,” says Mandal. “We were fascinated by just how robust the method is: it works exceptionally well for many types of computer generated granular materials. We are currently planning to extend this to experimental systems in the lab,” added Corneel Casert, joint first author Ghent University. Senior author, Professor Peter Sollich, Institute for Theoretical Physics, University of Göttingen, explains: “The efficiency of this new method is surprisingly high for different scenarios with varying system size, particle density, and composition of different particles types. This means it will be useful in understanding force chains for many types of granular matter and systems.”
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