machine learning

Physicists are exploring the opportunities that arise when the power of machine learning—a widely used approach in AI research—is brought to bear on quantum physics. Quantum physics often needs a description that approximately describes many interacting quantum particles. Two researchers at JQI presented new mathematical tools that will help researchers use machine learning to get such approximations and have identified new opportunities in quantum research where machine learning can be applied.

Researchers at the University of Maryland (UMD) have trained a small hybrid quantum computer to reproduce the features in a particular set of images. The result, which was published Oct. 18, 2019 in the journal Science Advances, is among the first demonstrations of quantum hardware teaming up with conventional computing power—in this case to do generative modeling, a machine learning task in which a computer learns to mimic the structure of a given dataset and generate examples that capture the essential character of the data.

In many situations, chaos makes it nearly impossible to predict what will happen next. Nowhere is this more apparent than in weather forecasts, which are notorious for their unreliability. But the clever application of artificial intelligence can help reign in some chaotic systems, making them more predictable than ever before.

In this episode of Relatively Certain, Dina sits down with Michelle Girvan, a physics professor at the University of Maryland (UMD), to talk about how artificial intelligence can help predict chaotic behavior, as well as how combining machine learning with conventional physics models might yield even better predictions and insights into both methods.