Computers Math

An international collaboration of researchers, led by Philip Walther at University of Vienna, have achieved a significant breakthrough in quantum technology, with the successful demonstration of quantum interference among several single photons using a novel resource-efficient platform. The work published in the journal Science Advances represents a notable advancement in optical quantum computing that paves the way for more scalable quantum technologies.
Interference among photons, a fundamental phenomenon in quantum optics, serves as a cornerstone of optical quantum computing. It involves harnessing the properties of light, such as its wave-particle duality, to induce interference patterns, enabling the encoding and processing of quantum information.
In traditional multi-photon experiments, spatial encoding is commonly employed, wherein photons are manipulated in different spatial paths to induce interference. These experiments require intricate setups with numerous components, making them resource-intensive and challenging to scale. In contrast, the international team, comprising scientists from University of Vienna, Politecnico di Milano, and Université libre de Bruxells, opted for an approach based on temporal encoding. This technique manipulates the time domain of photons rather than their spatial statistics. To realize this approach, they developed an innovative architecture at the Christian Doppler Laboratory at the University of Vienna, utilizing an optical fiber loop. This design enables repeated use of the same optical components, facilitating efficient multi-photon interference with minimal physical resources.
First author Lorenzo Carosini explains: “In our experiment, we observed quantum interference among up to eight photons, surpassing the scale of most of existing experiments. Thanks to the versatility of our approach, the interference pattern can be reconfigured and the size of the experiment can be scaled, without changing the optical setup.” The results demonstrate the significant resource efficiency of the implemented architecture compared to traditional spatial-encoding approaches, paving the way for more accessible and scalable quantum technologies. More

Tohoku University researchers have unveiled a new means of predicting how to synthesize new materials via the ion-exchange. Based on computer simulations, the method significantly reduces the time and energy required to explore for inorganic materials.
Details of their research were published in the journal Chemistry of Materials on April 17, 2024.
In the quest to form new materials that facilitate environmentally friendly and efficient energy technologies, scientists regularly rely on the high temperature reaction method to synthesize inorganic materials. When the raw substances are mixed and heated to very high temperatures, they are split into atoms and then reassemble into new substances. But this approach has some drawbacks. Only materials with the most energetically stable crystal structure can be formed, and it is not possible to synthesize materials that would decompose at high temperatures.
On the contrary, the ion-exchange method forms new materials at relatively low temperatures. Ions from existing materials are exchanged with ions of similar charge from other materials, thereby forming new inorganic substances. The low synthesis temperature makes it possible to obtain compounds that would not be available by the usual high temperature reaction method.
Despite its potential, however, the lack of a systematic approach to predicting appropriate material combinations for ion exchange has hindered its widespread adoption, necessitating laborious trial-and-error experiments.
“In our study, we predicted the feasibility of materials suited for ion exchange using computer simulations,” says Issei Suzuki, a senior assistant professor at Tohoku University’s Institute of Multidisciplinary Research for Advanced Materials, and co-author of the paper.
The simulations involved investigating the potential for ion exchange reactions between ternary wurtzite-type oxides and halides/nitrates. Specifically, Suzuki and his colleagues performed simulations on 42 combinations of β-MIGaO2, MI = Na, Li, Cu, Ag as precursors, and halides and nitrates as ion sources.
The simulation results were divided into three categories: “ion exchange occurs,” “no ion exchange occurs,” and “partial ion exchange occurs (solid solution is formed). To confirm their results, the researchers verified the simulation through actual experiments, confirming an agreement between simulation and experiments in all 42 combinations.
Suzuki believes that their advancement will accelerate the development of new materials suitable for improved energy technologies. “Our findings have shown that it is possible to predict whether ion exchange is feasible and to design reactions in advance without experimental trial and error. In the future, we plan to use this method to search for materials with new and attractive properties that will tackle energy problems.” More

A new robotic suction cup which can grasp rough, curved and heavy stone, has been developed by scientists at the University of Bristol.
The team, based at Bristol Robotics Laboratory, studied the structures of octopus biological suckers, which have superb adaptive suction abilities enabling them to anchor to rock.
In their findings, published in the journal PNAS today, the researchers show how they were able create a multi-layer soft structure and an artificial fluidic system to mimic the musculature and mucus structures of biological suckers.
Suction is a highly evolved biological adhesion strategy for soft-body organisms to achieve strong grasping on various objects. Biological suckers can adaptively attach to dry complex surfaces such as rocks and shells, which are extremely challenging for current artificial suction cups. Although the adaptive suction of biological suckers is believed to be the result of their soft body’s mechanical deformation, some studies imply that in-sucker mucus secretion may be another critical factor in helping attach to complex surfaces, thanks to its high viscosity.
Lead author Tianqi Yue explained: “The most important development is that we successfully demonstrated the effectiveness of the combination of mechanical conformation — the use of soft materials to conform to surface shape, and liquid seal — the spread of water onto the contacting surface for improving the suction adaptability on complex surfaces. This may also be the secret behind biological organisms ability to achieve adaptive suction.”
Their multi-scale suction mechanism is an organic combination of mechanical conformation and regulated water seal. Multi-layer soft materials first generate a rough mechanical conformation to the substrate, reducing leaking apertures to just micrometres. The remaining micron-sized apertures are then sealed by regulated water secretion from an artificial fluidic system based on the physical model, thereby the suction cup achieves long suction longevity on diverse surfaces but with minimal overflow.
Tianqi added: “We believe the presented multi-scale adaptive suction mechanism is a powerful new adaptive suction strategy which may be instrumental in the development of versatile soft adhesion.

“Current industrial solutions use always-on air pumps to actively generate the suction however, these are noisy and waste energy.
“With no need for a pump, it is well known that many natural organisms with suckers, including octopuses, some fishes such as suckerfish and remoras, leeches, gastropods and echinoderms, can maintain their superb adaptive suction on complex surfaces by exploiting their soft body structures.”
The findings have great potential for industrial applications, such as providing a next-generation robotic gripper for grasping a variety of irregular objects.
The team now plan to build a more intelligent suction cup, by embedding sensors into the suction cup to regulate suction cup’s behaviour. More

An entirely new predictive typing model can simulate different kinds of users, helping figure out ways to optimize how we use our phones. Developed by researchers at Aalto University, the new model captures the difference between typing with one or two hands or between younger and older users.
‘Typing on a phone requires manual dexterity and visual perception: we press buttons, proofread text, and correct mistakes. We also use our working memory. Automatic text correction functions can help some people, while for others they can make typing harder,’ says Professor Antti Oulasvirta of Aalto University.
The researchers created a machine-learning model that uses its virtual ‘eyes and fingers’ and working memory to type out a sentence, just like humans do. That means it also makes similar mistakes and has to correct them.
‘We created a simulated user with a human-like visual and motor system. Then we trained it millions of times in a keyboard simulator. Eventually, it learned typing skills that can also be used to type in various situations outside the simulator,’ explains Oulasvirta.
The predictive typing model was developed in collaboration with Google. New designs for phone keyboards are normally tested with real users, which is costly and time-consuming. The project’s goal is to complement those tests so keyboards can be evaluated and optimized more quickly and easily.
For Oulasvirta, this is part of a larger effort to improve user interfaces overall and understand how humans behave in task-oriented situations. He leads a research group at Aalto that uses computational models of human behaviour to probe these questions.
‘We can train computer models so that we don’t need observation of lots of people to make predictions. User interfaces are everywhere today — fundamentally, this work aims to create a more functional society and smoother everyday life,’ he says.
The researchers will present their findings at the CHI Conference in May. More

Contrary to previous assumptions, nerve cells in the human neocortex are wired differently than in mice. Those are the findings of a new study conducted by Charité — Universitätsmedizin Berlin and published in the journal Science.* The study found that human neurons communicate in one direction, while in mice, signals tend to flow in loops. This increases the efficiency and capacity of the human brain to process information. These discoveries could further the development of artificial neural networks.
The neocortex, a critical structure for human intelligence, is less than five millimeters thick. There, in the outermost layer of the brain, 20 billion neurons process countless sensory perceptions, plan actions, and form the basis of our consciousness. How do these neurons process all this complex information? That largely depends on how they are “wired” to each other.
More complex neocortex — different information processing
“Our previous understanding of neural architecture in the neocortex is based primarily on findings from animal models such as mice,” explains Prof. Jörg Geiger, Director of the Institute for Neurophysiology at Charité. In those models, the neighboring neurons frequently communicate with each other as if they are in dialogue. One neuron signals another, and then that one sends a signal back. That means the information often flows in recurrent loops.”
The human neocortex is much thicker and more complex than that of a mouse. Nonetheless, researchers had previously assumed — in part due to lack of data — that it follows the same basic principles of connectivity. A team of Charité researchers led by Geiger has now used exceptionally rare tissue samples and state-of-the-art technology to demonstrate that this is not the case.
A clever method of listening in on neuronal communication
For the study, the researchers examined brain tissue from 23 people who had undergone neurosurgery at Charité to treat drug-resistant epilepsy. During surgery, it was medically necessary to remove brain tissue in order to gain access to the diseased structures beneath it. The patients had consented to the use of this access tissue for research purposes.

To be able to observe the flows of signals between neighboring neurons in the outermost layer of the human neocortex, the team developed an improved version of what is known as the “multipatch” technique. This allowed the researchers to listen in on the communications taking place between as many as ten neurons at once (for details, see “About the method”). As a result, they were able to take the necessary number of measurements to map the network in the short time before the cells ceased their activity outside the body. In all, they analyzed the communication channels among nearly 1,170 neurons with about 7,200 possible connections.
Feed-forward instead of in cycles
They found that only a small fraction of the neurons engaged in reciprocal dialogue with each other. “In humans, the information tends to flow in one direction instead. It seldom returns to the starting point either directly or via cycles,” explains Dr. Yangfan Peng, first author of the publication. He worked on the study at the Institute for Neurophysiology and is now based at the Department of Neurology and the Neuroscience Research Center at Charité. The team used a computer simulation that they devised according to the same principles underlying the human network architecture to demonstrate that this forward-directed signal flow has benefits in terms of processing data.
The researchers gave the artificial neural network a typical machine learning task: recognizing the correct numbers from audio recordings of spoken digits. The network model that mimicked the human structures achieved more correct responses to this speech recognition task than the one modeled on mice. It was also more efficient, with the same performance requiring the equivalent of 380 neurons in the mouse model, but only 150 in the human one.
An economic role model for AI?
“The directed network architecture we see in humans is more powerful and conserves resources because more independent neurons can handle different tasks simultaneously,” Peng explains. “This means that the local network can store more information. It isn’t clear yet whether our findings within the outermost layer of the temporal cortex extend to other cortical regions, or how well they might explain the unique cognitive abilities of humans.”
In the past, AI developers have looked to biological models for inspiration in designing artificial neural networks, but have also optimized their algorithms independently of the biological models. “Many artificial neural networks already use some form of this forward-directed connectivity because it delivers better results for some tasks,” Geiger says. “It’s fascinating to see that the human brain also shows similar network principles. These insights into cost-efficient information processing in the human neocortex could provide further inspiration for refining AI networks.” More

An international research team led by scientists from the CNRS1 has discovered that the magnetic nanobubbles2 known as skyrmions can be moved by electrical currents, attaining record speeds up to 900 m/s.
Anticipated as future bits in computer memory, these nanobubbles offer enhanced avenues for information processing in electronic devices. Their tiny size3 provides great computing and information storage capacity, as well as low energy consumption.
Until now, these nanobubbles moved no faster than 100 m/s, which is too slow for computing applications. However, thanks to the use of an antiferromagnetic material4 as medium, the scientists successfully had the skyrmions move 10 times faster than previously observed.
These results, which were published in Science on 19 March, offer new prospects for developing higher-performance and less energy-intensive computing devices.
This study is part of the SPIN national research programme5 launched on 29 January, which supports innovative research in spintronics, with a view to helping develop a more agile and enduring digital world.
notes :
1 — The French laboratories involved are SPINTEC (CEA/CNRS/Université Grenoble Alpes), the Institut Néel (CNRS), and the Charles Coulomb Laboratory (CNRS/Université de Montpellier).

2 — A skyrmion consists of elementary nanomagnets (“spins”) that wind to form a highly stable spiral structure, like a tight knot.
3 — The size of a skyrmion can reach a few nanometres, which is to say approximately a dozen atoms.
4 — Antiferromagnetic stacks consist of two nano-sized ferromagnetic layers (such as cobalt) separated by a think non-magnetic layer, with opposite magnetisation.
5 — The SPIN priority research programme and equipment (PEPR) is an exploratory programme in connection with the France 2030 investment plan. More

With more than 200 types of cancer and every cancer individually unique, ongoing efforts to develop precision oncology treatments remain daunting. Most of the focus has been on developing genetic sequencing assays or analyses to identify mutations in cancer driver genes, then trying to match treatments that may work against those mutations.
But many, if not most, cancer patients do not benefit from these early targeted therapies. In a new study published on April 18, 2024, in the journal Nature Cancer, first author Sanju Sinha, Ph.D., assistant professor in the Cancer Molecular Therapeutics Program at Sanford Burnham Prebys, with senior authors Eytan Ruppin, M.D., Ph.D., and Alejandro Schaffer, Ph.D., at the National Cancer Institute, part of the National Institutes of Health (NIH) — and colleagues — describe a first-of-its-kind computational pipeline to systematically predict patient response to cancer drugs at single-cell resolution.
Dubbed PERsonalized Single-Cell Expression-Based Planning for Treatments in Oncology, or PERCEPTION, the new artificial intelligence-based approach dives deeper into the utility of transcriptomics — the study of transcription factors, the messenger RNA molecules expressed by genes that carry and convert DNA information into action.
“A tumor is a complex and evolving beast. Using single-cell resolution can allow us to tackle both of these challenges,” says Sinha. “PERCEPTION allows for the use of rich information within single-cell omics to understand the clonal architecture of the tumor and monitor the emergence of resistance.” (In biology, omics refers to the sum of constituents within a cell.)
Sinha says, “The ability to monitor the emergence of resistance is the most exciting part for me. It has the potential to allow us to adapt to the evolution of cancer cells and even modify our treatment strategy.”
Sinha and colleagues used transfer learning — a branch of AI — to build PERCEPTION.
“Limited single-cell data from clinics was our biggest challenge. An AI model needs large amounts of data to understand a disease, not unlike how ChatGPT needs huge amounts of text data scraped from the internet.”
PERCEPTION uses published bulk-gene expression from tumors to pre-train its models. Then, single-cell data from cell lines and patients, even though limited, was used to tune the models.

PERCEPTION was successfully validated by predicting the response to monotherapy and combination treatment in three independent, recently published clinical trials for multiple myeloma, breast and lung cancer.
In each case, PERCEPTION correctly stratified patients into responder and non-responder categories. In lung cancer, it even captured the development of drug resistance as the disease progressed, a notable discovery with great potential.
Sinha says that PERCEPTION is not ready for clinics, but the approach shows that single-cell information can be used to guide treatment. He hopes to encourage the adoption of this technology in clinics to generate more data, which can be used to further develop and refine the technology for clinical use.
“The quality of the prediction rises with the quality and quantity of the data serving as its foundation,” says Sinha. “Our goal is to create a clinical tool that can predict the treatment response of individual cancer patients in a systematic, data-driven manner. We hope these findings spur more data and more such studies, sooner rather than later.”
Additional authors on the study include Rahulsimham Vegesna, Sumit Mukherjee, Ashwin V. Kammula, Saugato Rahman Dhruba, Nishanth Ulhas Nair, Peng Jiang, Alejandro Schäffer, Kenneth D. Aldape and Eytan Ruppin, National Cancer Institute (NCI); Wei Wu, Lucas Kerr, Collin M. Blakely and Trever G. Biovona, University of California, San Francisco; Mathew G. Jones and Nir Yosef, University of California, Berkeley; Oleg Stroganov and Ivan Grishagin, Rancho BioSciences; Craig J. Thomas, National Institutes of Health; and Cyril H. Benes, Harvard University.
This research was supported in part by the Intramural Research Program of the NIH; NCI; and NIH grants R01CA231300, R01CA204302, R01CA211052, R01CA169338 and U54CA224081. More

Multiple sclerosis (MS) is an insidious disease. Patients suffer because their immune system is attacking their own nerve fibres, which inhibits the transmission of nerve signals. People with MS experience mild to severe impairment of their motor function and sensory perception in a variety of ways. These impairments disrupt their daily activities and reduce their overall quality of life. As individual as the symptoms and progression of the disease are, so too is the way it is managed. To monitor the disease progression and be able to recommend effective treatments, physicians ask their patients on a regular basis to describe their symptoms, such as fatigue.
Going off memory
Patients are thus faced with the tricky task of having to provide information about their state of health and what they have been capable of over the past few weeks and even months from memory. The data gathered in this way can be inaccurate and incomplete because patients might misremember details or tailor their responses to social expectations. And since these responses have a significant impact on how the progression of the disease is recorded, it could be mismanaged.
“Physicians would benefit from having access to reliable, frequent and long-term measurements of patients’ health parameters that give an accurate and comprehensive view of their state of health,” explains Shkurta Gashi. She is lead author of a new study and postdoc in the groups led by ETH Professors Christian Holz and Gunnar Rätsch at the Department of Computer Science as well as a fellow of the ETH AI Center.
Together with colleagues from ETH Zurich, the University Hospital Zurich, and the University of Zurich, Gashi has now shown that fitness trackers and smartphones can provide this kind of reliable long-term data with a high temporal resolution. Their study was published in the journal NPJ Digital Medicine.
Digital markers for MS
The researchers recruited a group of volunteers — 55 with MS and a further 24 serving as control subjects — and provided each person with a fitness tracking armband. Over the course of two weeks, the researchers collected data from these wearable devices as well as from participants’ smartphones. They then performed statistical tests and a machine learning analysis of this data to identify reliable and clinically useful information.

What proved particularly meaningful was the data on physical activity and heart rate, which was collected from participants’ wearable devices. The higher the participants’ disease severity and fatigue levels, the lower their physical activity and heart rate variability proved to be. Compared to the controls, MS patients took fewer steps per day, engaged in an overall lower level of physical activity and registered more consistent intervals between heartbeats.
How often people used their smartphone also delivered important information about their disease severity and fatigue levels: the less often a study participant used their phone, the greater their level of disability and the more severe their level of fatigue. The researchers gained insights into motor function through a game-like smartphone test. Developed at ETH a few years ago, this test requires the user to tap the screen as quickly as possible to make a virtual person move as fast as possible. Monitoring how fast a person taps and how their tapping frequency changes over time allows the researchers to draw conclusions about their motor skills and physical fatigue.
“Altogether, the combination of data from the fitness tracker and smartphone lets us distinguish between healthy participants and those with MS with a high degree of accuracy,” Gashi says. “Combining information related to several aspects of the disease, including physiological, behavioural, motor performance and sleep information, is crucial for more effective and accurate monitoring of the disease.”
Reliable approach
This new approach gives MS sufferers a straightforward way of collecting reliable and clinically useful long-term data as they go about their day-to-day lives. The researchers expect that this type of data can lead to better treatments and more effective disease management techniques: more comprehensive, precise and reliable data helps experts make better decisions and possibly even propose effective treatments sooner than before. What’s more, evaluating this patient data lets the experts verify the effectiveness of different treatments.
The researchers have now made their data set available to other scientists. They also point out the need for a larger study and more data to develop reliable and generalizable models for automatic evaluation. In the future, such models could enable MS patients to experience a significant improvement in their lives thanks to data from fitness trackers and smartphones. More