Articles tagged with Computational Physics
A team at the University of Washington has made a breakthrough in quantum computing by detecting signatures of 'fractional quantum anomalous Hall' (FQAH) states in semiconductor materials. This discovery marks a significant step towards building stable qubits and potentially developing fault-tolerant quantum computers.
Princeton researchers used data from NASA's Parker Solar Probe to determine that a catastrophic event, such as a high-speed collision or gaseous explosion, likely created the Geminids meteoroid stream. This is in contrast to most meteor showers, which originate from comets emitting tails of ice and dust.
Researchers developed a new technique called zero noise extrapolation (ZNE) that allows noisy quantum computers to produce accurate results for specific calculations. This breakthrough could enable the use of quantum computing for cutting-edge physics problems and improve classical algorithms.
A group of scientists from the JIHT, HSE and MIPT have developed a novel solution: OpenDust, a fast, open-source code that performs calculations ten times faster than existing analogues. The algorithm uses multiple GPUs simultaneously to accelerate computations.
A research group led by NCKU professor I-Non Chiu conducted the first cosmological study on galaxy clusters identified by eROSITA, analyzing 550 galaxy clusters. The results suggest that Dark Energy occupies up to 76% of the total energy density in the Universe.
The CHEP2023 conference will address computing, networking, and software issues for leading data-intensive science experiments. Key trends and solutions in computing as it applies to research in nuclear and high energy physics will be discussed.
Researchers from Osaka University developed an AI algorithm called FINDE that discovers and preserves the underlying conservation laws of real-world dynamical systems, not just superficial dynamics. FINDE allows for more accurate computer simulations and can reveal additional information about a system's structure.
Researchers developed a nano-excitonic transistor that controls excitons to process massive amounts of data at the speed of light with minimal heat energy loss. This technology has potential applications in optical computing and realizing an era of data explosion driven by AI.
Researchers have derived a formula predicting the effects of environmental noise on quantum computing. By incorporating redundancy in quantum messages, scientists can now quantify how much redundancy is needed to protect against dephasing noise.
The POSTECH team developed a multifunctional tip-enhanced spectroscopy that dynamically controls the physical properties of quasiparticles in 2D materials. This technology increases interlayer excitons' luminous efficiency by 9,000 times and modulates their energy.
A new mathematical model predicts turbulence and heat transport in fusion plasmas with high accuracy, approximately 1,500 times faster than conventional large-scale nonlinear calculations. This breakthrough accelerates fusion research and expands the range of applicability.
Researchers have developed a new simulation method to study polarons in 2D materials, which could lead to breakthroughs in OLED TVs and hydrogen fuel production. The study uses quantum mechanical theory and computation to determine the fundamental properties of polarons in 2D materials.
The team has developed a coupled dipole method-based photonic dispersion solver (CDPDS) online, enabling fast computation and accessibility to general users. The CDPDS provides band dispersions and topological phases of one-dimensional and two-dimensional photonic crystals, making it suitable for rapid simulations and modeling.
A new mathematical theory developed by Peter Wolynes and David Logan predicts the nature of motions in a chlorophyll molecule when it absorbs energy from sunlight. The findings suggest that there are exceptions where simple motions persist for long times, influencing processes like photosynthesis.
A new study by Tulane University demonstrates that even a single atom can act as a reservoir for computing, processing information optically. The researchers proposed a non-linear single-atom computer where input and output are encoded in light, enabling flexible computation with any desired outcome.
A new pumping strategy has been developed to slash energy costs of fluid transport by up to 22%. By switching pumps on and off, turbulent flows can be reduced, resulting in more efficient fluid transport. This approach could bring significant economic and environmental benefits, particularly for the transition to green energy.
A new study uses Fourier analysis to understand how deep neural networks learn complex physics. By analyzing the equation of a fully trained model, researchers were able to identify crucial information about how the network learns and generalizes. This breakthrough could accelerate the use of scientific deep learning in climate science.
A new Swedish quantum computer is being made available to the industry, accompanied by a test bed and a quantum helpdesk. The test bed will allow companies and researchers to solve problems using quantum technology at a significantly lower cost than existing commercial options.
Physicists at MIT and Caltech developed a new benchmarking protocol to characterize the fidelity of quantum analog simulators, enabling high precision characterization. The protocol analyzes random fluctuations in atomic-scale systems, revealing universal patterns that can be used to gauge the accuracy of these devices.
Researchers demonstrated high-visibility quantum interference between two independent semiconductor quantum dots, an important step toward scalable quantum networks. The observed interference visibility is up to 93%, paving the way for solid-state quantum networks with distances over 300 km.
Researchers used auto-encoder technique to analyze 150 XRD patterns of magnetic alloys, identifying clusters and fine-tuning alloys by detecting relevant peaks. The approach enables accelerated development of high-efficiency materials with low environmental impact.
A Polish-German-Italian team developed a new simulation tool called XSPIN to simulate X-ray-induced demagnetisation in multilayer materials. The tool allows for control over laser pulse parameters, such as energy and duration, to achieve specified spatial and temporal scales.
A team of quantum engineers at UNSW Sydney has developed a method to reset a quantum computer using a fast digital voltmeter to watch the temperature of an electron, reducing preparation errors from 20% to 1%. This innovation represents a modern twist on Maxwell's demon, a thought experiment that dates back to 1867.
A Collaborative Research Centre investigates animal navigation using the Earth's magnetic field. The study focuses on vertebrates, including birds and fish, aiming to protect endangered migratory species.
Researchers discover circular polycatenanes with properties similar to DNA rings, showcasing a connection between local and global properties. These structures have unique elastic properties and can be used in designing new materials and micro-sensors.
Researchers have developed a continuum theory of micro-hairs, allowing for the study of collective movements and fluid flows. The theory reveals that even random movement is unstable and leads to synchronisation, while perfect unison is also unstable, resulting in specific patterns of movement.
A team of researchers from Japan Advanced Institute of Science and Technology developed an analytical tool to investigate the ordering of fluorine in lead titanium oxyfluoride. They used first-principles calculation to analyze experimental results and determined the element substitution positions, finding that fluorine atoms predominan...
The study reveals that the distribution of local interface displacements exhibits non-zero skewness due to pinned segments lagging behind the rest. The researchers also found that scaling properties of interface segments depend on whether they are lagging or moving ahead of the average displacement.
Researchers have successfully demonstrated large numbers of interacting qubits maintaining coherence for an unprecedentedly long time, in a programmable solid state superconducting processor. This breakthrough could accelerate computing processes and enable applications such as quantum sensing and metrology.
Researchers from the Max Born Institute found that magnesium ions reduce ultrafast fluctuations in water's hydration shell, slowing solvation dynamics. The study reveals a short-range effect of individual ion pairs on dilute aqueous systems.
A breakthrough computer model from Chalmers University of Technology reveals the properties of an atomic nucleus, providing insights into the strong force that governs neutron star behavior. The model predicts a surprisingly thin neutron skin, which could lead to increased understanding of heavy element creation in neutron stars.
Physicists at the University of Basel have developed a computational shortcut for neural networks, allowing for faster calculation of optimal solutions without training. This breakthrough provides insight into neural network functioning and could help detect unknown phase transitions in materials and quantum systems.
A joint research team has proposed a method for densely storing data using a sharp probe, enabling polarization switching with minimal force. The result shows a significant increase in storage capacity, reaching up to 1 terabit per square centimeter.
Physicists used machine learning to compress a complex quantum problem into four equations, capturing the physics of electrons on a lattice with high accuracy. The approach could revolutionize how scientists investigate systems containing many interacting electrons and potentially aid in designing materials with sought-after properties.
Researchers at the University of Oldenburg and Fraunhofer IWES collaborate on a new project to develop more accurate wind flow simulations using artificial intelligence. The goal is to reduce computing times and enhance precision, ultimately accelerating innovation in wind turbine design.
Scientists reconstruct evolution of complex multicellular organisms using genomic data and computational models, revealing gradual changes that began early in evolution. The research reveals distinct paths taken by animal and fungal lineages, shedding light on their shared eukaryotic supergroup.
Physicists have created a way to simulate quantum entanglement between interacting particles using neural networks and fictitious 'ghost' electrons. This approach enables accurate predictions of molecule behavior, which could lead to breakthroughs in pharmaceutical development and material design.
KAUST researchers have developed a new method to simulate viscous liquids up to 15 times faster than the current state of the art. This breakthrough enables faster simulations for industrial processes, medical devices, computer graphics, and visual simulations.
A new AI program identified four variables for a swinging double-pendulum, but the remaining two variables remain a mystery. The AI successfully predicted physical phenomena in other systems, such as air dancers and lava lamps, with varying numbers of variables.
Scientists at Kyoto University propose a novel approach using holograms to approximate the universe's expansion in de Sitter space. The model uses conformal field theory and a positive integer for the cosmological constant, enabling the identification of the first example of two-dimensional CFT.
A Polish-Japanese team demonstrates a salutary delay in the reaction of crystal atoms to an avalanche of photons, using X-ray laser pulses. This discovery enables the observation of an undisturbed structure of matter by using sufficiently short laser pulses.
Scientists at Chung-Ang University have pioneered a novel method for controlling microdroplet motion on solid surfaces using near-infrared light. This approach allows for more precise control than traditional thermal techniques and opens up new possibilities for applications in microfluidics, drug delivery, and self-cleaning surfaces.
Researchers use computational detective work to verify the existence of a 3D quantum spin liquid in cerium zirconium pyrochlore, overcoming decades-long challenge. The material exhibits fractionalized spin excitations, where electrons do not arrange their spins in relation to neighbors.
Physicists from Cracow have developed a new measurement technique to track phenomena lasting attoseconds, using X-ray chronoscopy. This approach potentially makes it possible to infer events in the world of attophysics even at current XFEL technology.
Researchers from the University of Seville have conducted a groundbreaking experiment demonstrating quantum contextuality without loopholes. The study uses atomic ions to show that certain probabilities have a limit, contradicting previous findings.
Researchers used simulations to compare Einstein's theory and modified gravity, finding that 'dark gravity' may be equally good at explaining data from binary neutron star collisions. This could lead to the discovery of new phenomena detectable by next-generation gravitational interferometers.
Scientists have designed logic operations using liquid crystals, enabling potential applications in robotics and sensing. The technique uses topological defects to carry information, offering a new approach to computing.
Researchers at ETH Zurich have developed a new approach to modeling nonlinear dynamical systems using experimental data. By identifying key structures rather than detailed dynamics, the algorithm reduces calculation time from hours to just minutes.
Researchers at TU Darmstadt and Universitat Politècnica de València used HPC resources to develop a new symmetry-based turbulence theory, resolving the closure problem of turbulence. This approach allows for reduced computational grid size and direct access to mean values like air pressure and speed.
A team of researchers from Ritsumeikan University developed an unprecedented stream cipher using chaos theory to create highly secure cryptographic systems. The new system is resistant to statistical attacks and eavesdropping, even against quantum computers, making it a promising solution for post-quantum era cryptosystems.
Cornell researchers have successfully trained various physical systems, including mechanical, optical, and electrical systems, to perform machine learning tasks. The developed training algorithm enables diverse systems to be chained together for efficient processing.
Researchers at PPPL develop an algorithm to solve the complex equation describing free electron motion in tokamaks, enabling accurate simulations and better control of plasma. This breakthrough provides a rigorous mathematical proof and expands the capabilities of the Computational Sciences Department.
The ATIQ project aims to develop reliable, user-friendly quantum computing demonstrators based on ion trap technology within 30 months. The consortium will optimize hardware for applications in chemistry and finance, paving the way for new approaches in credit risk assessment.
A new machine learning-based algorithm can predict stable material compounds much faster than traditional methods, opening up new avenues for research and discovery. The researchers identified several thousand potential new compounds using the computer, offering a promising breakthrough in materials science.
The WVU-led Dolly Sods GPU cluster enables researchers to accelerate computational research in fields like drug development, interstellar phenomena, and biometrics. The cluster will facilitate the analysis of massive datasets and enable real-time processing of signals from satellites in space.
A team of researchers has developed a simple and efficient method of quantum encryption using single photons, which can detect any attempt to hack the message. The breakthrough brings us closer to securing our data against quantum computers' potential attacks.
New research reveals that sinking tectonic plates are significantly weakened as they enter the mantle, but not broken apart entirely. The study's computer model shows a 'tectonic snake' shape, with stresses pinching the plate along weak points.
Researchers have developed a new method that uses deep neural networks to predict extreme heat waves with unprecedented accuracy, up to two weeks before they occur. This breakthrough has significant implications for risk management, planning, and warning systems, which will greatly improve public safety and support public policies.
Researchers find that triangular-patterned materials can exhibit a mashup of three different phases, with each phase overlapping and competing for dominance. As temperature increases, the material becomes more ordered due to the breaking down of these competing electron arrangements.
Researchers have identified a complex alloy system that can be strengthened and made more ductile using quantum-mechanical modeling. This breakthrough may lead to more efficient engines, lowering fuel consumption and greenhouse gas emissions in the aviation industry.