Machine Learning Speeds Up Quantum Chemistry Calculations


A new quantum chemistry tool, called OrbNet, uses machine learning, quantum-chemistry calculations that can be performed 1,000 times faster than previously possible, allowing accurate quantum chemistry research to be performed faster than ever before. OrbNet was developed through a partnership between Tom Miller, Professor of Chemistry, and Anima Anandkumar, Bren Professor of Computing and Mathematical Sciences. [Caltech story]

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A Molecular Approach to Quantum Computing


The technology behind the quantum computers of the future is fast developing, with several different approaches in progress. Many of the strategies, or "blueprints," for quantum computers rely on atoms or artificial atom-like electrical circuits. In a new theoretical study in the journal Physical Review X, Caltech demonstrates the benefits of a lesser-studied approach that relies not on atoms but molecules. One concept behind the new research comes from work performed nearly 20 years ago by Professor John Preskill, Professor Alexei Kitaev, and their colleague Daniel Gottesman. [Caltech story]

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Advancing Future Quantum Science Efforts


Five new Department of Energy centers will apply quantum information science to emerging technologies. The centers will develop cutting-edge quantum technologies for use in a wide range of possible applications including scientific computing; fundamental physics and chemistry research; and the design of solar cells and of new materials and pharmaceuticals. Caltech faculty will participate in four of the new science centers: the Quantum Systems Accelerator, led by the Lawrence Berkeley National Laboratory, also known as Berkeley Lab; the Quantum Science Center, led by Oak Ridge National Laboratory; Q-NEXT, led by Argonne National Laboratory; and the Co-design Center for Quantum Advantage, led by Brookhaven National Laboratory. [Caltech story]

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AI for a Better Prediction COVID-19 Model


A team of Caltech students, led by Professor Yaser Abu-Mostafa, have developed a tool to predict the impact of COVID-19 using artificial intelligence (AI). While many models to predict the spread of a disease already exist, few if any incorporate AI, which makes predications based on observations of what is actually happening as opposed to what the model's designers think should happen. AI has the power to discover patterns hidden in data that the human eye might not recognize. [Caltech story]

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Machine Learning Helps Robot Swarms Coordinate


Soon-Jo Chung, Bren Professor of Aerospace, Yisong Yue, Professor of Computing and Mathematical Sciences, postdoctoral scholar Wolfgang Hönig, and graduate students Benjamin Rivière and Guanya Shi, have designed a new data-driven method to control the movement of multiple robots through cluttered, unmapped spaces, so they do not run into one another. "Our work shows some promising results to overcome the safety, robustness, and scalability issues of conventional black-box artificial intelligence (AI) approaches for swarm motion planning with GLAS and close-proximity control for multiple drones using Neural-Swarm," says Chung. [Caltech story]

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Professor Hou Featured in Quanta Magazine


The Quanta Magazine has featured Thomas Y. Hou, Charles Lee Powell Professor of Applied and Computational Mathematics, for his work in Euler singularity. Mathematicians and physicists have used Euler equations to model how fluids evolve over time. If you toss a rock into a still pond, how will the water be moving five seconds later? The Euler equations can tell you. Hou provided a numerical description of the initial state of a fluid and used a computer to apply the Euler equations to determine the fluid’s motion in the future. “From the top the fluid is spiraling down, and from the bottom it is swirling up in the opposite direction,” said Professor Hou. [Quanta article]

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Making a Better Match


Professor Adam Wierman worked with a cross-disciplinary team to improve the Pasadena Unified School District's open-enrollment algorithm. Wierman knew from experience that the district's open-enrollment process was not optimal. "I couldn't help but notice that it wasn't particularly well designed," says Wierman. "There was a huge opportunity, I thought, to improve." With the team's new and improved algorithm, families are more likely to get their top match and are also more likely to keep their children in the school district rather than enrolling them in private or charter schools. [Caltech story]

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Anandkumar Training Algorithms to Spot Online Trolls


Professor Anima Anandkumar, and research team have demonstrated that machine-learning algorithms can monitor online social media conversations as they evolve, which could one day lead to an effective and automated way to spot online trolling. "It was an eye-opening experience about just how ugly trolling can get. Hopefully, the tools we're developing now will help fight all kinds of harassment in the future," says Anandkumar. The research team includes Professor Michael Alvarez; Anqi Liu, postdoctoral scholar; Maya Srikanth, student; and Nicholas Adams-Cohen, Stanford University. [Caltech story]

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Researchers Develop New Quantum Algorithm


Austin Minnich, Professor of Mechanical Engineering and Applied Physics, Fernando Brandão, Bren Professor of Theoretical Physics, and Garnet Chan, Bren Professor of Chemistry, have developed an algorithm for quantum computers that will help them find use in simulations in the physical sciences. The new algorithm allows a user to find the lowest energy of a given molecule or material. Many people are interested in how to simulate the ground states of molecules and materials. "If we want to do a simulation of water, we could look at how water behaves after it has been blasted into a plasma—an electrically charged gas—but that's not the state water is usually found in; it is not the ground state of water. Ground states are of special interest in understanding the world under ordinary conditions," says Chan. [Caltech story]

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Caltech Announces the Schmidt Academy for Software Engineering


Caltech has launched the Schmidt Academy for Software Engineering to train the next generation of science-savvy software engineers and set new standards in scientific software. "This is a recognition that computing, software, and machine learning are going to play a very big role in science. Because Caltech is small and collaborative, we have the opportunity to really make a push in that direction," says Kaushik Bhattacharya, the Howell N. Tyson, Sr., Professor of Mechanics and Materials Science and vice provost. [Caltech release]

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