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AI model can manipulate time to make better predictions in a wide range of fields

In the world around us, many things exist in the context of time: a bird’s path through the sky is understood as different positions over a period of time, and conversations as a series of words occurring one after another.

Computer scientists and statisticians call these sequences time series. Although statisticians have found ways to understand these patterns and make predictions about the future, modern AI models struggle to perform just as well, if not worse, than statistical models.

Engineers at the University of California, Santa Cruz, developed a new method for time series forecasting, powered by deep learning, that can improve its predictions based on data from the near future. When they applied this approach to the critical task of seizure prediction using brain wave data, they found that their strategy offers up to 44.8% improved performance for predicting seizures compared to baseline methods. While they focused on this critical health care application, the researchers’ method is designed to be relevant for a wide range of fields. A new study in Nature Communications reports their results.

Combination of quantum and classical computing supports early diagnosis of breast cancer

Quantum computing is still in its early stages of development, but researchers have extensively explored its potential uses. A recent study conducted at São Paulo State University (UNESP) in Brazil proposed a hybrid quantum-classical model to support breast cancer diagnosis from medical images.

The work was published as part of the 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS), organized by the Institute of Electrical and Electronics Engineers (IEEE). In the publication, the authors describe a hybrid that combines quantum and classical layers using an approach known as a quanvolutional neural network (QNN). They applied the model to mammography and ultrasound images to classify lesions as benign or malignant.

“What we wanted to bring to this work was a very basic architecture that used quantum computing but contained a minimum of quantum and classical devices,” says Yasmin Rodrigues, the first author of the study. The work is part of her scientific initiation project, supervised by João Paulo Papa, full professor in the Department of Computing at the Bauru campus of UNESP. Papa also co-authored the article.

Entangled states enhance energy transfer in models of molecular systems

A study from Rice University, published in PRX Quantum, has found that energy transfers more quickly between molecular sites when it starts in an entangled, delocalized quantum state instead of from a single site. The discovery could lead to the development of more efficient light-harvesting materials that enhance the conversion of energy from light into other forms of energy.

Many , including photosynthesis, depend on rapid and efficient energy transfer following absorption. Understanding how quantum mechanical effects like entanglement influence these processes at room temperature could significantly change our approach to creating artificial systems that mimic nature’s efficiency.

“Delocalizing the initial excitation across multiple sites accelerates the transfer in ways that starting from a single site cannot achieve,” said Guido Pagano, the study’s corresponding author and assistant professor of physics and astronomy.

Heat-rechargeable design powers nanoscale molecular machines

Though it might seem like science fiction, scientists are working to build nanoscale molecular machines that can be designed for myriad applications, such as “smart” medicines and materials. But like all machines, these tiny devices need a source of power, the way electronic appliances use electricity or living cells use ATP (adenosine triphosphate, the universal biological energy source).

Researchers in the laboratory of Lulu Qian, Caltech professor of bioengineering, are developing nanoscale machines made out of synthetic DNA, taking advantage of DNA’s unique chemical bonding properties to build circuits that can process signals much like miniature computers. Operating at billionth-of-a-meter scales, these molecular machines can be designed to form DNA robots that sort cargos or to function like a neural network that can learn to recognize handwritten numerical digits.

One major challenge, however, has remained: how to design and power them for multiple uses.

Four central climate components are losing stability, says study

Four of the most important interconnected parts of the Earth’s climate system are losing stability, according to a review article based on observational data published in Nature Geoscience. The researchers succeeded in highlighting the warning signals for destabilization of the Greenland Ice Sheet, the Atlantic Meridional Overturning Circulation (AMOC), the Amazon rainforest, and the South American monsoon system.

Energy researchers discover fraction of an electron that drives catalysis

A team of researchers from the University of Minnesota Twin Cities College of Science and Engineering and the University of Houston’s Cullen College of Engineering has discovered and measured the fraction of an electron that makes catalytic manufacturing possible.

This discovery, published in the journal ACS Central Science, explains the utility of such as gold, silver and platinum for this manufacturing, and provides insight for designing new breakthrough catalytic materials.

Industrial catalysts—substances that reduce the amount of energy required for a given chemical reaction—allow producers to increase the yield, speed or efficiency of a specific reaction in pursuit of other materials. Such catalysts are used in processes related to pharmaceutical and battery production as well as petrochemical efforts such as the refining of crude oil, allowing supply to keep pace with demand in ways it otherwise could not.

New AI enhances the view inside fusion energy systems

Imagine watching a favorite movie when suddenly the sound stops. The data representing the audio is missing. All that’s left are images. What if artificial intelligence (AI) could analyze each frame of the video and provide the audio automatically based on the pictures, reading lips and noting each time a foot hits the ground?

That’s the general concept behind a new AI that fills in missing data about plasma, the fuel of fusion, according to Azarakhsh Jalalvand of Princeton University. Jalalvand is the lead author on a paper about the AI, known as Diag2Diag, that was recently published in Nature Communications.

“We have found a way to take the data from a bunch of sensors in a system and generate a synthetic version of the data for a different kind of sensor in that system,” he said. The synthetic data aligns with real-world data and is more detailed than what an actual sensor could provide. This could increase the robustness of control while reducing the complexity and cost of future fusion systems. “Diag2Diag could also have applications in other systems such as spacecraft and robotic surgery by enhancing detail and recovering data from failing or degraded sensors, ensuring reliability in critical environments.”

Spontaneous emission behaves contrary to predictions in photonic time crystals

A new study reveals that spontaneous emission, a key phenomenon in the interaction between light and atoms, manifests in a new form within a photonic time crystal. This research, led by a KAIST team, not only overturns existing theory but further predicts a novel phenomenon: spontaneous emission excitation. The findings are published in the journal Physical Review Letters.

Professor Bumki Min’s research team from the KAIST Department of Physics, in collaboration with Professor Jonghwa Shin of the Department of Materials Science and Engineering, Professor Wonju Jeon of the Department of Mechanical Engineering, Professor Gil Young Cho of the Department of Physics, and researchers from IBS, UC Berkeley, and the Hong Kong University of Science and Technology, announced that they have proven that the decay rate in a photonic time crystal is, on the contrary, enhanced rather than being “extinguished,” as suggested by a paper published in Science in 2022. Furthermore, they predicted a new process—spontaneous emission excitation—where an atom transitions from its to an while simultaneously emitting a photon.

Spontaneous emission is the process by which an atom intrinsically emits a photon and is fundamental to quantum optics and photonic device research. Until now, control over spontaneous emission has been achieved by designing spatial structures like resonators or . However, the advent of photonic time crystals, which periodically modulate the refractive index of a medium over time, has drawn attention to the potential for control along the time axis.

Parallel atom-photon entanglement paves way for future quantum networking

A new platform developed by Illinois Grainger engineers demonstrates the utility of a ytterbium-171 atom array in quantum networking. Their work represents a key step toward long-distance quantum communication.

Researchers from The Grainger College of Engineering at the University of Illinois Urbana-Champaign have introduced a scalable platform for quantum networking with a ytterbium-171 array.

Their work, published in Nature Physics, represents a major step toward larger quantum networks and has promising implications for modular quantum computation.

Dark matter detector succeeds in performing measurements with nearly no radioactive interference

In their search for dark matter, scientists from the XENON Collaboration are using one of the world’s most sensitive dark matter detectors, XENONnT at the Gran Sasso Laboratory of the National Institute of Nuclear Physics INFN in Italy, to detect extremely rare particle interactions. These could provide clues about the nature of dark matter. The problem, however, is that tiny amounts of natural radioactivity generate background events that can mask these weak signals.

The XENONnT experiment has made a breakthrough by significantly reducing one of the most problematic contaminants— , a radioactive gas. For the first time, the research team has succeeded in reducing the detector’s radon-induced radioactivity to a level a billion times lower than the very low natural radioactivity of the human body.

The underlying technology, which the XENONnT consortium reports in the current issue of the Physical Review X, was developed by a team led by particle physicist Prof Christian Weinheimer from the University of Münster.

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