In the PRISMA++ Cluster of Excellence, Professor Stefan Weinzierl and colleagues report that their approach accelerates computations dramatically—by roughly a factor of 1,000. The impact is practical as well as theoretical: depending on the scattering process, researchers may need to evaluate from thousands up to nearly a million integrals. A speedup at this scale can turn previously infeasible calculations into routine workflows.

The core idea is to sort integrals using intrinsic geometric properties rather than relying on “ad-hoc” labels. Weinzierl likens the method to organizing a library by content: instead of sorting books by superficial metadata, the algorithm “looks inside” each integral by analyzing its geometric structure. This internal geometric viewpoint enables computer algebra programs to simplify the governing equations much more effectively.

Technically, the method is built as a two-step algorithm. First, a new geometric order relation guides the reduction of integrals toward a basis of so-called master integrals. Once expressed in this structured basis, the associated differential equations can be written as a Laurent polynomial in the regularization parameter ε (epsilon).

Second, the team introduces a procedure to “trivialize” the ε-dependence of those differential equations. Together, the steps yield an epsilon-factorized form—an arrangement known to be easier to integrate systematically and reliably. The authors emphasize that the procedure is algorithmic, meaning it can be applied across a wide class of Feynman integrals rather than being tailored to a single problem.

The result is a more scalable computational pipeline for precision calculations in particle physics. Weinzierl notes that the method can enable predictions for far more processes than previously possible, extending the reach of theoretical efforts supporting cutting-edge measurements at facilities such as the Large Hadron Collider.

Subject of Research: Not applicable
Article Title: New algorithms for Feynman integral reduction and epsilon-factorized differential equations
News Publication Date: 15-Jun-2026
Web References: http://dx.doi.org/10.1103/mjpn-61yv
References: 10.1103/mjpn-61yv
Image Credits: Ill./©: JGU
Keywords: Feynman integrals, epsilon-factorized differential equations, master integrals, computer algebra, geometric ordering, particle physics precision, PRISMA++

Now, an international team led by the Center for Quantum Nanoscience (QNS) at the Institute for Basic Science (IBS) has demonstrated electrical control of an individual magnetic molecule. Collaborators at Karlsruhe Institute of Technology (KIT) used electron spin resonance in combination with scanning tunnelling microscopy (ESR-STM) to probe how a voltage applied at the nanoscale can reshape a molecule’s spin behavior.

The researchers focused on iron phthalocyanine (FePc) and closely related spin-carrying complexes on a surface. By sweeping the voltage with fine precision, they observed that the spin response was not weak and linear—as earlier electric-field approaches often were—but instead became strongly nonlinear near specific molecular electronic energy levels.

At the heart of the effect is an exchange-mediated interaction between the molecular spin and the magnetic STM tip. As the voltage tunes the system toward an electronic resonance, this exchange coupling boosts the effective spin splitting, producing resonance-frequency shifts approaching 30%. That magnitude is roughly an order of magnitude larger than most prior electrically induced tuning effects in molecular spin systems.

Crucially, the data also supports a theoretical framework advanced at QNS: voltage-controlled exchange interactions can generate a highly localized effective magnetic field without physically deforming the molecule. Because the interaction is mediated through a nearby electrode, the control region stays confined to the target spin.

Beyond shifting energy levels, the team performed coherent control. Using Rabi-oscillation measurements, they showed that individual molecular spins can be selectively driven by electrical voltages rather than by changing external magnetic fields. They further tuned coupled spins without significantly disturbing a neighboring molecule, a key requirement for scalable quantum architectures.

Unlike strategies that depend on manipulating the molecule’s geometry, this exchange-driven method offers a practical route toward nanoscale quantum circuitry. Electrical signals are naturally easier to route and integrate than localized magnetic fields, opening a path for dense device layouts.

The work appears in Nature Physics, providing a new mechanism for spin–electric control at the single-molecule level and strengthening the roadmap toward atom- and molecule-based quantum computing, quantum sensing, and quantum information processing.

Subject of Research: Not applicable
Article Title: Exchange-mediated spin–electric control of single molecules on surfaces
News Publication Date: 29-Jul-2026
Web References: http://dx.doi.org/10.1038/s41567-026-03353-w
References: https://doi.org/10.1038/s41567-026-03353-w
Image Credits: Institute for Basic Science

Keywords: qubits, quantum computing, quantum information processing, quantum information science, spin manipulation, scanning tunneling microscopy, exchange-mediated control, single-molecule quantum control

The study draws on data from 2,410 participants in the Framingham Heart Study, a long-running community cohort. Researchers modeled sleep duration alongside blood p-tau181 measurements while adjusting for multiple health and demographic factors, aiming to isolate the association from confounders.

Rather than producing a simple “more sleep equals more biomarker” pattern, the results show a curve. Sleep durations beginning around 8.5 to 9 hours were associated with higher p-tau181 levels, with the steepest rise occurring beyond 10 hours per night. This suggests that very long sleep may be a behavioral marker of early disease-related changes.

Lead author Vanessa M. Young cautions that the work is observational and captures a single point in time. That means the study cannot prove that longer sleep causes Alzheimer’s. Still, the authors argue that sleep patterns could be clinically useful for flagging individuals who may benefit from closer cognitive and biomarker monitoring.

To uncover the relationship, the team used flexible non-linear statistical approaches rather than forcing a straight-line assumption. Specifically, restricted cubic splines were applied to estimate how the sleep–biomarker link evolves across the range of sleep durations.

Importantly, the researchers tested whether similar patterns appeared for other Alzheimer- and neurodegeneration-related blood proteins. The sleep association disappeared for these markers once kidney function was considered, leaving p-tau181 as the main signal that remained robust after adjustment.

Young and colleagues interpret this specificity as potentially pointing toward Alzheimer-related biology rather than a broad effect of physiology on protein clearance. However, they emphasize that replication and prospective validation are needed before any clinical conclusions can be drawn.

The study appears amid a growing debate about whether sleep that is too short or too long harms brain health. Earlier work from the same research ecosystem suggested that sleeping nine hours or more could coincide with worse cognitive performance, especially in people with depression.

While the research does not prescribe sleep duration changes, it adds to a viral-ready narrative: sleep is not only about rest—it may also mirror underlying molecular changes. For clinicians and the public, the takeaway is a conversation starter—especially for those regularly sleeping 9 to 10 hours or more.

Subject of Research: Alzheimer’s disease; sleep duration; blood biomarkers (p-tau181)

Article Title: Non-linear associations between sleep duration and plasma p-tau181 in the Framingham Heart Study

News Publication Date: 16-July-2026 (article text); study published 19-May-2026

Web References: https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/alz.71499

References: 10.1002/alz.71499

Image Credits: Not provided in the provided content

Keywords: Alzheimer’s disease, sleep duration, p-tau181, phosphorylated tau, non-linear modeling, biomarkers, restricted cubic splines, Framingham Heart Study, neurodegeneration

The work brings together teams from the University of Chicago Pritzker School of Molecular Engineering, Harvard, Stony Brook University, and Quantinuum. Using non-Abelian anyons encoded across multiple qubits, the researchers show that by moving (braiding) these excitations in carefully chosen patterns—and combining that with additional operations—they can implement the full range of gates needed for arbitrary quantum algorithms.

“We demonstrated a universal gate set,” said Ruben Verresen of UChicago PME, explaining that storing information in these emergent quark-like degrees of freedom and then manipulating them enables essentially any quantum computation. The goal is not just proof that quantum effects can be controlled, but that the control is broad enough to scale into general-purpose computing.

A central motivation is fault tolerance. Conventional quantum error correction protects qubits by spreading information across many physical qubits, but universal gate sets usually require resource-heavy “magic states.” Building those states typically involves distillation procedures that consume significant machine time and qubits—one of the biggest practical costs in leading architectures.

Non-Abelian anyons are naturally attractive because their information is distributed across entangled degrees of freedom, making them comparatively resilient to local noise. Just as importantly, their braiding can function as computation. Yet prior demonstrations using the D4 symmetry group—based on rotations and reflections of a square—showed that braiding alone was not enough to reach full universality.

The new study targets a different symmetry, S3, associated with rotations and mirror flips of an equilateral triangle. On Quantinuum’s H2 trapped-ion processor, the team entangled 54 qubits to realize S3-based anyons. Crucially, the researchers show that universality emerges only when braiding is paired with fusion, a measurement-like operation where two anyons are merged and the outcome is read out.

To benchmark the approach, the team encoded information in “topological qutrits,” which use three quantum levels rather than the two levels of ordinary qubits. Braiding produced an entangling operation, while fusion generated distinct measurement operations; together, these components can in principle synthesize any quantum transformation, including gates unreachable by braiding alone. The protocol also enables preparation of a magic state directly via topological operations, potentially avoiding expensive distillation.

In the current results, the researchers did not perform active error correction. Instead, they verified key building blocks and confirmed that a magic state produced through anyon-based procedures matches theoretical expectations. “So far, we’ve ignored the question of error correction,” Verresen said—framing the work as a proof of principle.

The next step is to integrate this anyon-based approach with error correction to move from demonstrated primitives to scalable, fault-tolerant computation. Verresen and collaborators are already exploring methods to stabilize non-Abelian quantum memories, aiming to make this “dark horse” architecture practical for large-scale quantum machines.

Subject of Research: Universal quantum computation with non-Abelian anyons (braiding and fusion) and implications for fault-tolerant quantum error correction
Article Title: Universal gates from braiding and fusing anyons on quantum hardware
News Publication Date: 15-Jul-2026
Web References: https://www.nature.com/articles/s41586-026-10709-y
References: Lo et al., Nature (July 15, 2026). DOI: 10.1038/s41586-026-10709-y
Image Credits:
Keywords: quantum computing; non-Abelian anyons; topological qutrits; universal gate set; quantum error correction; braiding and fusion; trapped-ion processor

The research focuses on the rare gudgeon (Gobiocypris rarus), a small freshwater fish endemic to China’s Yangtze River Basin. Because of its limited distribution and high sensitivity to environmental change, it has been classified as rare and endangered. Instead of running more laboratory exposures, the team built a machine learning enhanced quantitative structure–activity relationship (ML-QSAR) model tailored to this species.

To train the system, the authors assembled acute and chronic toxicity datasets for G. rarus. They then generated over 1,800 molecular descriptors capturing physicochemical and structural properties—such as electronic behavior, polarity, and interaction-relevant features—along with the fish’s developmental stage (embryos, juveniles, adults). This stage-aware design allows the model to reflect that biology is not static across development.

Six algorithms were compared, including random forest, support vector machines, neural networks, and generalized linear models. The random forest model delivered the strongest performance, reaching a coefficient of determination of 0.99 for acute toxicity and 0.93 for chronic toxicity, indicating accurate structure-to-effect predictions under limited experimental coverage.

A key finding is that acute and chronic toxicity are governed by different drivers. Life stage proved highly influential for short-term effects, consistent with the idea that embryos and juveniles have less mature metabolism and detoxification pathways. However, the sensitivity pattern varies across chemical classes, and adults may retain certain PFAS-like compounds longer due to stronger protein binding.

For chronic outcomes, the model leaned more heavily on molecular interaction descriptors. These features relate to how chemicals move, accumulate, and bind at the target level—considering properties such as ionization potential, polarizability, and atomic arrangement.

The authors then applied the best model to 73 pollutants reported in the rare gudgeon’s habitat, including many per- and polyfluoroalkyl substances (PFAS). For 12 PFAS compounds with available environmental concentration data, calculated risk quotients were far below 1, suggesting low immediate ecological risk under current measurements.

Importantly, the study warns against interpreting low quotients as “safe.” PFAS are persistent, can biomagnify across food webs, and may rise due to industrial shifts, seasonal effects, or replacement chemistry. The work therefore supports continued long-term monitoring of PFAS distribution and bioaccumulation in this ecosystem.

Finally, the ML-QSAR strategy offers a scalable blueprint for conservation science: integrate chemical fingerprints with developmental biology and machine learning to flag potential threats before populations experience irreversible harm. Future efforts will expand toxicity datasets, evaluate chemical mixtures, and improve performance for metals and newly emerging contaminants.

Subject of Research: Machine learning-QSAR for toxicity prediction and ecological risk assessment in endangered fish
Article Title: Toxicity prediction and ecological risk assessment of new contaminants to rare and endangered species using machine learning-QSAR: a case study of conserving Gobiocypris rarus in the Yangtze River Basin
News Publication Date: 30-Apr-2026
Web References: https://doi.org/10.48130/newcontam-0026-0010
References: Wang Y, Wang X, Zhou Y, Cheng Y, Li X, et al. 2026. New Contaminants 2: e015. doi:10.48130/newcontam-0026-0010
Image Credits: Ying Wang, Xin Wang, Yunchi Zhou, Yinghao Cheng, Xiaomin Li, Xiaolei Wang, Yuefei Ruan, Zhaomin Dong & Wenhong Fan

Keywords: machine learning, ML-QSAR, toxicity prediction, ecological risk assessment, rare gudgeon, PFAS, conservation, random forest, developmental stage, quantitative structure–activity relationship

A new study from the Hebrew University of Jerusalem challenges that assumption. Using ultra-sensitive tunneling spectroscopy, researchers probed the superconducting energy spectrum with high resolution, searching for subtle deviations from the expected single-gap behavior. The measurements revealed features that could not be reconciled with conventional single-order descriptions.

Instead, the data point to an unexpected mechanism: the materials host two superconducting states that are strongly interacting. Rather than acting independently, these two orders become coupled so effectively that their combined signatures mimic what looks like one clean superconducting gap. In other words, the system “disguises” its complexity, producing an experimental fingerprint similar to a simpler superconductor.

The team applied a more sophisticated two-band superconductivity framework to match both the detailed spectral line shape and how the superconducting state evolves in the presence of magnetic fields. This modeling clarified why earlier theories struggled to reproduce the full form of the energy spectrum, even when they captured some aspects of the response.

The results were consistent across both compounds, suggesting the phenomenon is not an isolated anomaly of NbSe₂. In TaS₂, the same hidden two-order structure emerges, implying a broader principle for superconductivity in certain layered transition-metal dichalcogenides.

The study also raises a tantalizing implication for the bulk form of NbSe₂. While the thin samples show two coupled superconducting orders, the thicker material may contain three interacting superconducting orders, implying an even richer hierarchy of electronic states beyond the minimal two-gap picture.

Beyond resolving a long-standing puzzle, the work provides a practical guide for interpreting superconducting spectra in materials where multiple orders can masquerade as one. This matters for the design of future superconducting devices, especially in regimes where precise control of electronic behavior is crucial.

As superconductivity moves toward applications in quantum technologies and ultra-efficient electronics, the ability to accurately identify hidden order parameters could improve material engineering. Understanding what electrons actually “do” inside these compounds may be the next step toward building superconductors with predictable, tunable performance.

Subject of Research:

Article Title: Two-Band Superconductivity in Few-Layer NbSe₂ and TaS₂

News Publication Date:

Web References: http://dx.doi.org/10.1103/p836-tdgw

References:

Image Credits: Avigail Ben Eliyahu

Keywords: superconductivity, quantum matter, condensed matter physics, superconducting energy gap, spectroscopy, tunneling spectroscopy, NbSe₂, TaS₂, two-band superconductivity, quantum computing

The analysis drew on six independent cohorts of cognitively unimpaired older adults, using longitudinal follow-up to evaluate how P‑tau217 relates to later impairment across multiple time horizons. Importantly, the association remained statistically significant even after adjusting for amyloid measured by PET imaging, suggesting the marker captures disease-relevant biology beyond amyloid status alone.

Because Alzheimer’s disease develops gradually, the ability to forecast risk over clinically meaningful windows could help clinicians and researchers target prevention efforts earlier. In this work, P‑tau217 functioned as a blood-based surrogate connected to amyloid and tau-related disease processes, reinforcing the view that tau pathology is not merely a late-stage phenomenon.

The authors argue that, if ongoing secondary prevention trials demonstrate that early intervention can delay or prevent cognitive decline, risk estimates derived from blood biomarkers like P‑tau217 could help identify which individuals are most likely to benefit. Such a strategy may also reduce reliance on resource-intensive procedures for large-scale screening.

However, the researchers emphasize that current risk projections are based on selected cohorts rather than broad population sampling. They also note that confidence is stronger for shorter projections than for longer ones, with estimates for 10 years generally more uncertain.

The modeling approach also faces important limitations, including incomplete accounting for vascular comorbidities and the competing risk of death, both of which can influence whether cognitive impairment is observed during follow-up. Additionally, P‑tau217 may not fully reflect non-Alzheimer contributors to cognitive impairment, including vascular effects and other neurodegenerative processes.

In parallel, the Alzheimer’s Association’s current clinical practice guidance cautions against testing cognitively unimpaired older adults outside research settings or clinical trials. While the results are promising for advancing prevention research, the clinical use of P‑tau217 is not yet endorsed.

The study’s publication DOI is 10.1001/jama.2026.12556. Media outlets can request interviews with contributing author Rachel F. Buckley, PhD, and corresponding author Reisa Sperling, MD, through the listed institutional media contacts.

Subject of Research: Alzheimer’s disease risk prediction using plasma biomarkers (P‑tau217)
Article Title: Not provided
News Publication Date: July 15, 2026
Web References: Not provided
References: doi:10.1001/jama.2026.12556
Image Credits: Not provided

Keywords: P‑tau217, plasma biomarker, Alzheimer’s disease, cognitive impairment, amyloid PET adjustment, tau pathology, prevention trials, older adults, risk modeling

Most current workflows build layered 2D stacks using sticky synthetic polymers to hold atomically thin crystals in place. While effective, polymer residues can remain trapped at interfaces, subtly altering electronic behavior and masking the intrinsic properties researchers are trying to measure.

Instead of polymers, the researchers used muscovite, a natural mineral commonly known as mica, as the stacking platform. Because mica is inorganic and crystalline, it provides atomically flat surfaces and reduces the formation of microscopic debris at the critical interfaces between different 2D layers.

In experiments, substituting polymers with mica produced heterostructures with significantly improved surface cleanliness and flatness. The result is a more reliable physical foundation for creating precisely aligned layered systems, where the relative twist angle between components strongly determines electronic and quantum phenomena.

The team emphasizes that controlled alignment is essential for engineered states such as exotic superconductivity and tunable magnetism, which emerge in carefully constructed 2D stacks. Even tiny amounts of contamination can suppress or obscure these effects, making interface purity a decisive requirement for reproducible quantum-material experiments.

Lead author Dr Makars Šiškins notes that the method is not only cleaner but also potentially cheaper, lowering the barrier for high-precision device assembly. He argues that improved alignment and cleanliness open the door to complex heterostructures that were previously too challenging to manufacture routinely.

Co-lead Prof Alexey Berdyugin adds that mica’s crystalline nature avoids many of the contamination issues associated with soft polymer layers. By enabling ultra-clean electronic interfaces, the technique helps components operate closer to their full designed potential.

The study was published in Nature Communications and appears as part of a broader push toward fabrication strategies that can support the next generation of quantum processors and faster, more dependable nanoelectronics. If adopted widely, polymer-free van der Waals assembly could accelerate both fundamental research and the engineering of future quantum technologies.

Subject of Research: Not applicable
Article Title: Polymer-free van der Waals assembly of 2D material heterostructures using muscovite crystals
News Publication Date: 4-May-2026
Web References: http://www.nature.com/articles/s41467-026-72554-x ; http://dx.doi.org/10.1038/s41467-026-72554-x
References: 10.1038/s41467-026-72554-x
Image Credits: University of Southampton

Keywords: Quantum computing; Quantum processors; 2D heterostructures; van der Waals assembly; mica; graphene; hexagonal boron nitride; device fabrication; electronic interfaces; contamination control

Shear stress occurs when neighboring parts of a material are displaced relative to one another, generating internal stress. Until now, much research treated shear-driven damage as limited, which made it difficult to fully explain why components sometimes deteriorate faster than expected when subjected to shear-dominated conditions.

To reveal the mechanism, the team focused on aluminum alloy AA2198-T851, a material valued for transportation applications, especially in aviation. They first applied tensile loading to initiate microstructural voids, then switched to shear loading to observe how those pre-formed damage features evolved over time.

The experiments demonstrated a dramatic effect: voids at intermetallic particles continued to enlarge during shear, with the total void volume increasing up to sixfold. Rather than allowing the metal’s internal structure to reorganize and reduce stress concentrations, the stiff intermetallic particles restrained material movement and effectively boosted the rate and extent of void expansion.

The researchers did not rely on indirect measurements. Instead, they combined synchrotron computed laminography (SR-CL)—a technique akin to high-resolution 3D X-ray imaging—with advanced three-dimensional simulation. SR-CL enables visualization inside centimeter-scale, flat samples with micrometer-range detail, making it possible to track voids and their spatial relationship to particle networks.

In parallel, finite element modeling supported interpretation of the evolving microstructure, connecting observed damage patterns to mechanics under shear. Together, imaging and simulation offered a coherent picture of how particles promote void growth rather than merely serving as passive inclusions.

“Our results show a previously unknown damage route in metals under shear,” said Dr. Mathias Hurst from IPS. “Contamination in the form of stiff particles can induce significant damage growth under shear loading.”

By clarifying this particle-induced pathway, the study provides guidance for designing components that better resist failure in real-world loading scenarios. It also highlights a practical challenge for sustainability: recycled metals may contain abundant intermetallic particles, potentially increasing vulnerability to shear-driven degradation.

Original publication: Particle-induced void growth under shear loading revealed by 3D X-ray laminography and finite element modeling, International Journal of Plasticity (2026). DOI: 10.1016/j.ijplas.2026.104724.

Subject of Research: Damage mechanisms in metals under shear loading; particle-induced void growth
Article Title: Particle-induced void growth under shear loading revealed by 3D X-ray laminography and finite element modeling
News Publication Date: 15-Jun-2026
Web References: https://doi.org/10.1016/j.ijplas.2026.104724
References: International Journal of Plasticity (2026), DOI: 10.1016/j.ijplas.2026.104724
Image Credits: Simon Bode, KIT
Keywords: shear stress, void growth, intermetallic particles, aluminum alloy, synchrotron computed laminography, 3D imaging, finite element modeling, material damage, metal recycling, ductility

Entanglement, the quintessential quantum phenomenon where particle states exhibit correlations beyond classical explanation, is essential for scalable quantum computers and quantum networks. Traditionally, generating entanglement over distance involved either sending a single photon actively controlled from one qubit to another or matching photons emitted independently by two qubits. While these approaches, especially the latter recognized by the 2022 Nobel Prize in Physics, have propelled the field forward, they depend heavily on repeated measurements and post-selection, often limiting entanglement availability and reliability.

In contrast, the ISTA team engineered a new configuration in which the qubits interact with a common source of correlated photons forming a quantum bath. This environment autonomously “locks” the qubits into an entangled state continuously, maintaining coherence even beyond the qubits’ natural lifetimes. The non-local squeezed reservoir effectively creates a stable ground state that the qubits inhabit, ensuring entanglement is perpetually accessible, a critical feature for future quantum technologies.

Their prototype uses microwave photons, ideal for circuit-based superconducting qubits, to implement this novel scheme. Unlike optical photons commonly used in long-distance quantum communication, microwave photons suit the manipulation of stationary quantum bits and underpin many of today’s leading quantum processors. By bridging continuous-variable entanglement—described by smoothly varying properties—and discrete-variable entanglement—an all-or-nothing quantum correlation of qubits—the researchers have addressed a longstanding mismatch in quantum computing architectures.

To validate entanglement formation, the team employed quantum tomography, a sophisticated technique reconstructing qubit states from rapid, repeated measurements lasting mere nanoseconds. This analysis confirmed the synchronized states predicted by theory, definitively proving that the quantum bath can sustain distributed entanglement autonomously.

Although the current method captures about 10% of the quantum bath’s potential entanglement, it showcases a notably simple and scalable approach. The researchers suggest that their setup can be expanded to synchronize larger networks of qubits, promising a pathway toward fault-tolerant quantum computation.

This experimental realization not only closes a 20-year gap between theoretical proposal and laboratory demonstration but also opens new avenues for quantum optics experimentation and the scaling of quantum processors. By providing on-demand, long-lived entanglement without complex feedback, the ISTA breakthrough may redefine how quantum information technologies are built and operated.

Subject of Research: Not applicable
Article Title: Distributing stationary qubit entanglement through a non-local squeezed reservoir
News Publication Date: 13-Jul-2026
Web References: DOI: 10.1103/r4jt-j39w
Image Credits: © ISTA
Keywords: Qubits, Quantum computing, Quantum mechanics, Quantum entanglement, Photons