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LANL


A new theorem from the field of quantum machine learning has poked a major hole in the accepted understanding about information scrambling. “Our theorem implies that we are not going to be able to use quantum machine learning to learn typical random or chaotic processes, such as black holes. In this sense, it places a …

SeismoGen, a machine learning technique developed at the Laboratory, is capable of generating high-quality synthetic seismic waveforms. The technique could save tedious and intensive manual labeling effort and help improve earthquake detection. A new machine-learning model that generates realistic seismic waveforms will reduce manual labor and improve earthquake detection, according to a study published recently …

PEGASUS, the Portable EnGineered Analytic Sensor with aUtomated Sampling, is a miniaturized waveguide-based optical sensor that can detect toxins, bacterial signatures, viral signatures, biothreats, white powders and more, from samples such as blood, water, CSF, food, and animal samples. A device from Los Alamos National Laboratory researchers is not quite the Star Trek “tricorder” medical …

The U.S. transportation industry is the nation’s largest generator of greenhouse gases, accounting for nearly one-third of climate-warming emissions. To move towards a clean-energy future, developing zero-emissions technologies for heavy-duty vehicles is critical. A new partnership comprising Los Alamos National Laboratory, Advent Technology Holdings Inc., Brookhaven National Laboratory, and the National Renewable Energy Laboratory (NREL) …

A Los Alamos National Laboratory-designed spectroscopy instrument allows scientists, industry, and governments to decipher even trace amounts of chemicals using the Earth’s own magnetic field. Called the Earth-field Resonance Detection and Evaluation device (ERDE, which is German for “Earth”), the instrument is the most sensitive, affordable, and portable technology of its kind, with the ability …

Making sense of vast streams of big data is getting easier, thanks to an artificial-intelligence tool developed at Los Alamos National Laboratory. SmartTensors sifts through millions of millions of bytes of diverse data to find the hidden features that matter, with significant implications from health care to national security, climate modeling to text mining, and …

Automated data set generation provides a highly diverse sampling of atomic positions for training an accurate and general machine learning model. A revolutionary machine-learning (ML) approach to simulate the motions of atoms in materials such as aluminum is described in this week’s Nature Communications journal. This automated approach to “interatomic potential development” could transform the …

A team of quantum theorists seeking to cure a basic problem with quantum annealing computers—they have to run at a relatively slow pace to operate properly—found something intriguing instead. While probing how quantum annealers perform when operated faster than desired, the team unexpectedly discovered a new effect that may account for the imbalanced distribution of …

Using machine learning to develop algorithms that compensate for the crippling noise endemic on today’s quantum computers offers a way to maximize their power for reliably performing actual tasks, according to a new paper. “The method, called noise-aware circuit learning, or NACL, will play an important role in the quest for quantum advantage, when a …

Using a trained neural network and data from the North Anatolian Fault in Turkey, a research team led by Los Alamos National Laboratory revealed the first direct observation of rupture propagation during a slow earthquake. The research will be presented Dec. 15, 2020 at the AGU Fall Meeting. Applying AI to interferometric synthetic aperture radar …