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Nanomagnets control diamond qubits, pointing to more scalable quantum hardware

Quantum computing, once only a theoretical possibility, promises to deliver faster, more energy-efficient computers—but only if scientists can build and scale the hardware needed to run the machines. New research from Virginia Commonwealth University brings scientists one small step closer to quantum computing at a practical scale, which could help dramatically reduce energy usage and computing times in some industries.

In the study, recently published in Nature Communications, the researchers used minuscule magnets—twice as small as the wavelength of light—to create the building blocks of quantum computing, pioneering a technique that could decrease the physical space needed to create a viable quantum computer.

“This work has the potential to advance quantum computing,” said Jayasimha Atulasimha, Ph.D., a professor of mechanical and nuclear engineering in VCU’s College of Engineering and the study’s principal investigator. “We’re solving a specific problem for spin-based quantum computing, which has the potential for scaling.”

Study Suggests Spacetime Can Crystallize Possibly Solving Several Mysteries

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Hello and welcome! My name is Anton and in this video, we will talk about crystallization of spacetime.
Links:
https://journals.aps.org/prl/pdf/10.1
#science #physics #spacetime.

0:00 Can spacetime crystallize?
0:35 So what is this then?
1:55 Let’s define the main terms and phenomena: spacetime.
2:30 Crystals.
2:55 Spacetime crystal.
3:50 Previous challenges and propositions.
5:10 Main achievement in the study.
6:10 What does any of this mean for us?
7:10 Solving singularity and quantum gravity?
8:05 Explaining dark matter?
8:45 JWST observations.
9:28 Any proof? Gravitational waves!
11:55 Conclusions.

Enjoy and please subscribe.

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Critical Thalamocortical Coordination Dynamics Track Conscious State Transitions

Abstract Despite substantial progress in identifying neural correlates of consciousness, no unified quantitative framework currently derives a formally specified order parameter for conscious-state organisation from established neurophysiological principles, or links thalamocortical coordination dynamics to measurable state transitions across pharmacological, pathological, and perturbational conditions through a single computational formalism. We propose a neurocomputational theoretical framework in which conscious states are associated with metastable regimes of large-scale thalamocortical coordination operating near critical dynamical boundaries. The framework is formalised through a dynamic coordination functional Φ(t), defined as a surface integral over the thalamocortical interface and directly operationalisable from high-density EEG as a weighted combination of gamma-band power spectral density, thalamocortical coherence, and theta-gamma phase-amplitude coupling. The thalamic reticular nucleus (TRN) is identified as the anatomical implementation of the control parameter governing proximity to the critical point, grounded in a Wilson-Cowan model of TRN inhibitory gating whose bifurcation structure is characterised computationally. Numerical simulation of the linearised field equation on the thalamocortical boundary demonstrates internal consistency: the simulated system produces power-law recovery dynamics tau_rec proportional to | θ — θ _c|^v with nu consistent with model A universality class [0.5, 1.5], and a Kuramoto mean-field derivation establishes that Φ(t) emerges as the natural order parameter of coupled thalamocortical oscillators rather than being postulated. The joint (|Φ(t)|, Var[|Φ(t)|]) phase space correctly separates simulated waking, anaesthetic, ictal, and minimally conscious regimes without parameter fitting to empirical data. All simulation code is publicly available. Six quantitatively specific, independently falsifiable predictions are derived across five experimental domains: power-law Gamma Dip scaling in near-threshold EEG with a specific exponent range; causal disruption of thalamocortical coherence by selective TRN silencing; opposite EEG scaling exponent deviations in ASD versus schizophrenia; systematic Φ_est collapse under propofol anaesthesia correlated with PCI; Φ_est as a real-time consciousness biomarker in disorders of consciousness; and clinical validity of Φ_est in disorders of consciousness and ictal state discrimination by the metastability index. Each prediction is stated with quantitative thresholds and a pre-specified falsification criterion. The framework provides: the first anatomically specified and formally derived order parameter for conscious-state organisation directly operationalisable from passive EEG; a mechanistically grounded identification of the TRN as the dynamical control parameter, testable by a single optogenetic experiment; and a computationally validated, pre-registerable programme of six falsifiable predictions defining a tractable empirical agenda. Φ_est would constitute a candidate real-time consciousness biomarker if the framework’s predictions are confirmed in purpose-designed experiments.

Predicting physics without parameter tuning: A faster computational approach

Numerical simulations in physics often require estimating a multitude of parameters, making the process computationally expensive and complex. Researchers at University of Tsukuba have introduced a new method called the multiparameter eigenvalue-problem emulator, enabling reliable predictions based directly on relationships among known data by eliminating the need for parameter estimation. This innovation considerably reduces computational costs and enables systematic quantification of predictive uncertainty.

Calibrating theoretical models with experimental data is a common practice in physics for predicting previously unobserved phenomena. However, real-world theoretical models are often highly complex, involving numerous numerical quantities, known as parameters, that cannot be directly measured. Researchers must estimate these parameters to compute other observables. This is a process that is computationally demanding and fraught with remarkable challenges in assessing how uncertainties in the parameters affect final predictions.

This study, published in Physical Review Letters, presents a novel fast surrogate model based on a mathematical framework known as the multiparameter eigenvalue-problem emulator. This model directly predicts unknown observables based on relationships among known data, without the need to introduce or estimate parameters.

Microsoft’s Coreutils project brings Linux commands to Windows

Microsoft announced today at its Build 2026 developer conference the release of Coreutils for Windows, bringing many commonly used Linux command-line utilities to Windows as native applications.

The project is based on the open-source uutils project, a cross-platform rewrite of the GNU coreutils in Rust, and is designed to make it easier for developers to switch between Linux, macOS, Windows, and Windows Subsystem for Linux (WSL) without changing workflows.

“Developers constantly move between platforms, but familiar commands don’t work consistently, forcing workarounds, lost speed and context switching,” announced Microsoft.

E.W. Dijkstra Archive: On the cruelty of really teaching computing science (EWD 1036)

For Dijkstra, programming was closer to mathematics than to a craft. The goal wasn’t to “get a feel” for code. The goal was to reason about it rigorously, to understand why it works before discovering whether it works.


The second part of this talk pursues some of the scientific and educational consequences of the assumption that computers represent a radical novelty. In order to give this assumption clear contents, we have to be much more precise as to what we mean in this context by the adjective “radical”. We shall do so in the first part of this talk, in which we shall furthermore supply evidence in support of our assumption.

The usual way in which we plan today for tomorrow is in yesterday’s vocabulary. We do so, because we try to get away with the concepts we are familiar with and that have acquired their meanings in our past experience. Of course, the words and the concepts don’t quite fit because our future differs from our past, but then we stretch them a little bit. Linguists are quite familiar with the phenomenon that the meanings of words evolve over time, but also know that this is a slow and gradual process.

It is the most common way of trying to cope with novelty: by means of metaphors and analogies we try to link the new to the old, the novel to the familiar. Under sufficiently slow and gradual change, it works reasonably well; in the case of a sharp discontinuity, however, the method breaks down: though we may glorify it with the name “common sense”, our past experience is no longer relevant, the analogies become too shallow, and the metaphors become more misleading than illuminating. This is the situation that is characteristic for the “radical” novelty.

Innovative Mars rovers ‘swim’ through the sand

Some animals can move efficiently beneath granular surfaces. These include the sandfish (Scincus scincus), a lizard native to the Sahara. It can burrow into the sand and then literally “swim” through the desert sand to hunt or escape predators.

The principles of movement underlying this ability have only been understood for a few years. Researchers at the University of Würzburg have now translated the sandfish’s locomotion mechanism into an initial technical solution—an innovative Mars rover that outperforms other models when moving on sand.

The team led by computer scientist Marco Schmidt, Professor for Embedded Systems and Sensors for Earth Observation (ESSEO), is collaborating with researchers from Bremen. The project is part of the VaMEx initiative of the German Aerospace Center.

Axial encoding unlocks up to eightfold faster 3D microscopy with less light

A research team from HKU Engineering has pioneered a fundamentally new imaging strategy known as AIMED (Arbitrary illumination microscopy with encoded depth), which utilizes a sub-sampling approach. By integrating innovations in axial optical encoding with advanced computational image reconstruction, the AIMED technology enables a substantial increase in 3D imaging speed while enhancing photon safety, all with minimal additional system complexity. This breakthrough demonstrates significant advantages across efficiency, image quality, and system compatibility.

This work was conducted by the OMEGA laboratory under the leadership of Professor Kenneth K. Y. Wong of the Department of Electrical and Computer Engineering at the University of Hong Kong (HKU). The study is published in the journal Advanced Photonics.

Better math discriminates exotic from classical materials

The planar Hall effect is a tabletop diagnostic tool for special quantum properties useful in basic research and technological applications. Or so it was thought, because careful calculation by Kobe University researchers clarifies the conditions under which this effect may also appear in classical materials. This makes the diagnostic more meaningful and enables more purposeful design.

In the hunt for materials with properties that are useful for quantum computing or spintronics, researchers have used the “planar Hall effect” as a tabletop diagnostic tool: The researchers send a current through a thin, flat sample and observe whether an electric voltage is produced in response to a magnetic field in the same plane as the sample.

If it is, the pattern of how the voltage responds to rotating the magnetic field in the plane of the sample tells researchers about the properties of the material.

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