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Category: computing – Page 4

Microsoft claims new quantum chip 1,000 times better than before

At the heart of quantum computing are qubits, which offer the promise of answering questions that defeat today’s machines, but are notoriously delicate and unstable.

Microsoft says the qubits on Majorana 2, its new chip, survive for an average of 20 seconds, rather than the milliseconds of Majorana 1.

That means the new chip is 1,000 times more reliable — an improvement in performance the tech giant compares to the difference between a phone that needs charging every day to one which needs charging every few years.

Ultrafast laser shrinks to chip scale, potentially lowering costs for diagnostics and atomic clocks

Ultrafast lasers emit pulses lasting only a few hundred femtoseconds (quadrillionths of a second). These flashes of light power applications from precision micromachining to eye surgery to optical frequency combs, the Nobel Prize-winning technology behind today’s most precise optical atomic clocks. Yet despite more than two decades of effort, ultrafast lasers have largely remained bulky, expensive systems confined to optical tables.

Now a team led by Professor Tobias J. Kippenberg at EPFL has brought them onto a photonic chip. Publishing in Nature, the researchers report the first integrated ultrafast laser to rival tabletop femtosecond lasers, delivering 1.05 nanojoules in pulses as short as 147 femtoseconds.

Photonic chips guide and process light in microscopic channels called waveguides patterned on a wafer, similar to how electronic microchips route electricity. Already widely used in telecommunications, photonic chips have miniaturized complex functions that once required much larger systems.

‘Don’t scare the cat!’ Engineers find smarter way to measure quantum systems

UNSW Sydney engineers have riffed on the famous Schrödinger’s cat analogy to demonstrate a more efficient way to eliminate errors in quantum computing.

“Imagine you’re trying to find your cat hiding in one of eight identical cardboard boxes, in a dark and noisy room,” says UNSW Scientia Professor Andrea Morello.

“You are not allowed to enter the room—opening the door may kill the cat. What is the optimal strategy to find out where it’s hiding? Our team of quantum researchers have found an answer to this problem, and it might be an important milestone on the road to building a quantum computer.”

Open-source software unlocks rapid DNA structure generation and analysis in one workflow

Computational chemists at the University of Amsterdam’s Van ‘t Hoff Institute for Molecular Sciences have developed a comprehensive software suite to create accurate models of DNA in biomolecular assemblies. Called MDNA, the user-friendly molecular modeling toolkit helps biochemists, molecular biologists, bioinformaticians, and biophysicists to visualize and analyze DNA structures and perform accurate simulations.

The development of the MDNA suite, led by associate professor Jocelyne Vreede, has been presented in a paper in Nucleic Acids Research.

The software is open-source and publicly available through Figshare and Github. It is easily accessible, providing inspiration to any scientist with an interest in DNA. It has been thoroughly tested by students in mathematics, chemistry and biology, some of whom had hardly any programming experience.

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.

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