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Category: information science

A quintillion calculations a second. That’s one with 18 zeros after it. It’s the speed at which an exascale supercomputer will process information. The Department of Energy (DOE) is preparing for the first exascale computer to be deployed in 2021. Two more will follow soon after. Yet quantum computers may be able to complete more complex calculations even faster than these up-and-coming exascale computers. But these technologies complement each other much more than they compete.

It’s going to be a while before quantum computers are ready to tackle major scientific research questions. While quantum researchers and scientists in other areas are collaborating to design quantum computers to be as effective as possible once they’re ready, that’s still a long way off. Scientists are figuring out how to build qubits for quantum computers, the very foundation of the technology. They’re establishing the most fundamental quantum algorithms that they need to do simple calculations. The hardware and algorithms need to be far enough along for coders to develop operating systems and software to do scientific research. Currently, we’re at the same point in that scientists in the 1950s were with computers that ran on vacuum tubes. Most of us regularly carry computers in our pockets now, but it took decades to get to this level of accessibility.

In contrast, exascale computers will be ready next year. When they launch, they’ll already be five times faster than our fastest —Summit, at Oak Ridge National Laboratory’s Leadership Computing Facility, a DOE Office of Science user facility. Right away, they’ll be able to tackle major challenges in modeling Earth systems, analyzing genes, tracking barriers to fusion, and more. These powerful machines will allow scientists to include more variables in their equations and improve models’ accuracy. As long as we can find new ways to improve conventional computers, we’ll do it.

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Researchers from the Institute of Industrial Science at The University of Tokyo designed and built specialized computer hardware consisting of stacks of memory modules arranged in a 3D-spiral for artificial intelligence (AI) applications. This research may open the way for the next generation of energy-efficient AI devices.

Machine learning is a type of AI that allows computers to be trained by example data to make predictions for new instances. For example, a smart speaker algorithm like Alexa can learn to understand your voice commands, so it can understand you even when you ask for something for the first time. However, AI tends to require a great deal of electrical energy to train, which raises concerns about adding to climate change.

Now, scientists from the Institute of Industrial Science at The University of Tokyo have developed a novel design for stacking resistive random-access memory modules with oxide semiconductor (IGZO) access transistor in a three-dimensional spiral. Having on-chip nonvolatile memory placed close to the processors makes the machine learning training process much faster and more energy-efficient. This is because electrical signals have a much shorter distance to travel compared with conventional computer hardware. Stacking multiple layers of circuits is a natural step, since training the algorithm often requires many operations to be run in parallel at the same time.

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A drone has successfully inspected a 19.4 meter high oil tank onboard a Floating Production, Storage and Offloading vessel. The video shot by the drone was interpreted in real-time by an algorithm to detect cracks in the structure.

Scout Drone Inspection and class society DNV GL have been working together to develop an autonomous drone system to overcome the common challenges of tank inspections. For the customer, costs can run into hundreds of thousands of dollars as the tank is taken out of service for days to ventilate and construct scaffolding. The tanks are also tough work environments, with surveyors often having to climb or raft into hard to reach corners. Using a drone in combination with an algorithm to gather and analyse video footage can significantly reduce survey times and staging costs, while at the same time improving surveyor safety.

“We’ve been working with drone surveys since 2015,” said Geir Fuglerud, director of ofshore classification at DNV GL – Maritime. “This latest test showcases the next step in automation, using AI to analyse live video. As class we are always working to take advantage of advances in technology to make our surveys more efficient and safer for surveyors, delivering the same quality while minimising our operational downtime for our customers.”

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PNNL quantum algorithm theorist and developer Nathan Wiebe is applying ideas from data science and gaming hacks to quantum computing.

Everyone working on quantum computers knows the devices are error prone. The basic unit of quantum programming – the quantum gate – fails about once every hundred operations. And that error rate is too high.

While hardware developers and programming analysts are fretting over failure rates, PNNL’s Nathan Wiebe is forging ahead writing code that he is confident will run on quantum computers when they are ready. In his joint appointment role as a professor of physics at the University of Washington, Wiebe is training the next generation of quantum computing theorists and programmers.

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DeepMind this week released Acme, a framework intended to simplify the development of reinforcement learning algorithms by enabling AI-driven agents to run at various scales of execution. According to the engineers and researchers behind Acme, who coauthored a technical paper on the work, it can be used to create agents with greater parallelization than in previous approaches.

Reinforcement learning involves agents that interact with an environment to generate their own training data, and it’s led to breakthroughs in fields from video games and robotics to self-driving robo-taxis. Recent advances are partly attributable to increases in the amount of training data used, which has motivated the design of systems where agents interact with instances of an environment to quickly accumulate experience. This scaling from single-process prototypes of algorithms to distributed systems often requires a reimplementation of the agents in question, DeepMind asserts, which is where the Acme framework comes in.

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Breaking the lowest oxygen abundance record.

New results achieved by combining big data captured by the Subaru Telescope and the power of machine learning have discovered a galaxy with an extremely low oxygen abundance of 1.6% solar abundance, breaking the previous record of the lowest oxygen abundance. The measured oxygen abundance suggests that most of the stars in this galaxy formed very recently.

To understand galaxy evolution, astronomers need to study galaxies in various stages of formation and evolution. Most of the galaxies in the modern Universe are mature galaxies, but standard cosmology predicts that there may still be a few galaxies in the early formation stage in the modern Universe. Because these early-stage galaxies are rare, an international research team searched for them in wide-field imaging data taken with the Subaru Telescope. “To find the very faint, rare galaxies, deep, wide-field data taken with the Subaru Telescope was indispensable,” emphasizes Dr. Takashi Kojima, the leader of the team.

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Skoltech scientists have shown that quantum enhanced machine learning can be used on quantum (as opposed to classical) data, overcoming a significant slowdown common to these applications and opening a “fertile ground to develop computational insights into quantum systems.” The paper was published in the journal Physical Review A.

Quantum computers utilize quantum mechanical effects to store and manipulate information. While quantum effects are often claimed to be counterintuitive, such effects will enable quantum enhanced calculations to dramatically outperform the best supercomputers. In 2019, the world saw a prototype of this demonstrated by Google as quantum computational superiority.

Quantum algorithms have been developed to enhance a range of different computational tasks; more recently this has grown to include quantum enhanced machine learning. Quantum machine learning was partly pioneered by Skoltech’s resident-based Laboratory for Quantum Information Processing, led by Jacob Biamonte, a coathor of this paper. “Machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that are thought not to produce efficiently, so it is not surprising that quantum computers might outperform classical computers on machine learning tasks,” he says.

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Fascinating interview with Dutch astronomer Anthony Brown on ESA’s Gaia satellite and what it’s telling us about our own Milky Way Galaxy.

Dutch astronomer Anthony Brown of Leiden University explains how the European Space Agency’s GAIA satellite is revolutionizing what we know about the Milky Way. This all-sky survey mission revisits each target 70 times over the course of the years-long mission to give astronomers a real 3D map of a large swath of our galaxy. The next big data drop is scheduled by year’s end.

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Quantum computers have enormous potential for calculations using novel algorithms and involving amounts of data far beyond the capacity of today’s supercomputers. While such computers have been built, they are still in their infancy and have limited applicability for solving complex problems in materials science and chemistry. For example, they only permit the simulation of the properties of a few atoms for materials research.

Scientists at the U.S. Department of Energy’s (DOE) Argonne National Laboratory and the University of Chicago (UChicago) have developed a method paving the way to using quantum computers to simulate realistic molecules and complex materials, whose description requires hundreds of atoms.

The research team is led by Giulia Galli, director of the Midwest Integrated Center for Computational Materials (MICCoM), a group leader in Argonne’s Materials Science division and a member of the Center for Molecular Engineering at Argonne. Galli is also the Liew Family Professor of Electronic Structure and Simulations in the Pritzker School of Molecular Engineering and a Professor of Chemistry at UChicago. She worked on this project with assistant scientist Marco Govoni and graduate student He Ma, both part of Argonne’s Materials Science division and UChicago.

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