Los Alamos National Laboratory Archives - Liwaiwai

Quantum Machine Learning Hits A Limit

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 fundamental limit on the learnability of unknown processes,” said Zoe Holmes, a post-doc at Los Alamos National Laboratory and coauthor of the paper describing the work published today in Physical Review Letters. “Thankfully, because most physically interesting processes are sufficiently simple or structured so that…

Machine Learning Model Generates Realistic Seismic Waveforms

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 in JGR Solid Earth. “To verify the eﬃcacy of our generative model, we applied it to seismic ﬁeld data collected in Oklahoma,” said Youzuo Lin, a computational scientist in Los Alamos National Laboratory’s Geophysics group and principal investigator of the project. “Through a sequence of…

Technology

New Biosensor Designed To Detect Toxins And More

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 scanner, but it’s a step in the right direction. The Portable EnGineered Analytic Sensor with aUtomated Sampling (PEGASUS) 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,…

Technology

Moving Toward A Clean-Energy Future By Advancing Fuel Cell Technology

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) will work over the next few years to bring to market high-temperature proton exchange membrane (HT-PEM) fuel cells that convert hydrogen and other renewable fuels into electricity. “As the heavy-duty transportation industry seeks greener alternatives to combustion engines, HT-PEM fuel cells promise a clean, efficient…

New Machine Harnesses Earth’s Magnetic Field To Detect Chemicals

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 to detect a range of chemicals, including those commonly used in scientific labs, biological weapons, and even slight traces of insecticides in drinking water. “Using the Earth’s magnetic field allows us to do several things that were not possible before,” said Derrick Kaseman, a scientist…

New AI Tool Makes Vast Data Streams Intelligible And Explainable

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 many other fields. “SmartTensors analyzes terabytes of diverse data to find the hidden patterns and features that make the data understandable and reveal its underlying processes or causes,” said Boian Alexandrov, a scientist at Los Alamos National Laboratory, AI expert, and principal investigator on the…

Machine Learning Aids In Simulating Dynamics Of Interacting Atoms

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 field of computational materials discovery. “This approach promises to be an important building block for the study of materials damage and aging from first principles,” said project lead Justin Smith of Los Alamos National Laboratory. “Simulating the dynamics of interacting atoms is a cornerstone of…

Lack Of Symmetry In Qubits Can’t Fix Errors In Quantum Computing, But Might Explain Matter/Antimatter Imbalance

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 matter and antimatter in the universe and a novel approach to separating isotopes. “Although our discovery did not cure the annealing time restriction, it brought a class of new physics problems that can now be studied with quantum annealers without requiring they be too slow,”…

Machine Learning

Machine Learning Blazes Path To Reliable Near-term Quantum Computers

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 quantum computer solves a problem that’s impossible on a classical computer,” said Patrick Coles, a quantum physicist in at Los Alamos National Laboratory and lead author on the paper, “Machine learning of noise-resilient quantum circuits,” published today in Physical Review X Quantum. “Our work automates…

AI Reveals First Direct Observation Of Rupture Propagation During Slow Quakes

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 (InSAR) images opens up a new way to unravel the physics behind tectonic faults and earthquakes, which is key to understanding the full spectrum of earthquake behavior. “The deep-learning approach we developed makes it possible to automatically detect the small and transient deformation that occurs…

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