Physicist studies society 鈥 electron society. Her specialty is quantum condensed matter physics, which deals with particles the size of atoms or smaller.
By harnessing the power of machine learning to analyze data produced by experiments into electron behavior, , professor of physics in the 麻豆视频 and 麻豆视频 (A&S), together with collaborators in A&S, the College of Engineering and the Cornell Ann S. Bowers College of Computing and Information Science, is leading the way toward applications of quantum mechanics, including the discovery of new quantum materials and the development of quantum computing.
Kim recently received an $800,000 grant from the Gordon and Betty Moore Foundation for a project titled 鈥淎ccelerating Machine-Learning-Driven Discovery in Quantum Materials.鈥 With the Foundation support, Kim will build on her work in developing machine learning tools to solve problems in quantum computing related to qubits, originally supported by a grant from the philanthropically funded program in A&S. Kim鈥檚 grant was funded by Justice Miriam Shearing '56.
The complicated characteristics of electrons that make them difficult to understand also make them powerful in developing valuable materials and computing tools 鈥 the way each has an inherent spin, for example, or the way two or more electrons can share a seemingly telepathic 鈥渆ntangled鈥 bond. But experiments into these and other qualities of quantum materials produce particularly difficult data sets.
鈥淒ata problems can arise in two forms, one in the form of volume, the other in the form of complexity,鈥 said Kim. Machine learning, she said, can help with both problems.
On the high-volume front, Kim analyzed 10 terabytes of data on strongly correlated quantum matter (SCQM), produced by the and Argonne National Lab with , associate professor of computer science.
鈥淭he volume of information in the entire Library of Congress is about 20 terabytes,鈥 Kim said. 鈥淚n this experiment, we worked with 10 terabytes of data from a tiny piece of material.鈥
Kim has also developed machine learning solutions for particularly complex data sets. In a recent collaboration, Kim and doctoral student Michael Matty produced a machine-learning algorithm that could analyze data from an experiment by the Dick & Dale Reis Johnson Assistant Professor of physics (A&S) (URu2Si2) to 17.5 kelvin (minus 256 degrees Celsius). Data from this experiment was not especially large in volume, but was difficult to analyze.
鈥淯ranium ruthenium silicide has puzzling behavior,鈥 Kim said. 鈥淚t looks like something is happening, but we can鈥檛 tell quite what is happening. That鈥檚 called 鈥榟idden order.鈥 It was hard to interpret; that鈥檚 where we used machine learning.鈥
The analysis by Kim and Matty uncovered underlying patterns in the data. This allowed the team to eliminate about half of the more than 20 explanations for the material鈥檚 hidden order. Moreover, it proved that machine learning can be a useful approach to difficult data analysis problems in experimental physics.
Machine learning is also changing Kim鈥檚 approach to the field of quantum computing. She and collaborators are using machine learning to gain understanding into the quantum storage unit called a qubit.
鈥淥ne of the promising aspects of quantum computing is that you can handle a much larger volume of information when you use qubits,鈥 Kim said.
A qubit has the potential to encode exponentially more information than a bit, the classical computing storage unit, in the way a ball has infinitely many more positions on its surface than a two-pole bar magnet, Kim said.
There are several different ways of engineering qubits, Kim said. One existing platform uses the electron鈥檚 spin 鈥 its inherent angular momentum. Others, including developments by IBM and Google, use a superconducting-based qubit.
No matter what the method of engineering, Kim said, retrieving information that鈥檚 been encoded in qubits proves difficult: 鈥淎lthough there is a tantalizing possibility of being able to encode a lot of information, reading it out is a non-trivial process with qubits.鈥
, Kim and Weinberger, together with Harvard University researchers, show the enormous number of possibilities a set number of qubits present for information storage. They鈥檝e developed a machine learning tool to parse quantum matter and make crucial distinctions in the data 鈥 an approach that will help future explorations into subatomic phenomena.
鈥淭he information that can be encoded in 169 qubits is 2 to the power of 169. That鈥檚 larger than the number of stars in the observable universe, by far,鈥 Kim said. 鈥淭his paper exemplifies how we can use machine learning to understand a state of many qubits.鈥
Even with all these recent and future developments, quantum computing is at the seedling stage, Kim said. In the same way that the first inventors of classical computing could not imagine the iPhone, researchers working on quantum condensed matter physics can鈥檛 imagine what applications might lead to, she said.
With , associate professor of applied and engineering physics (Engineering), and Alumni Affairs & Development, Kim has shaped a vision for an institute in quantum information at Cornell to boost innovation on quantum information and technology. Kim, Fuchs and their colleagues in A&S and Engineering envision a cross-college institute that unites three focus areas 鈥 quantum theory, quantum experiment and quantum technology.
鈥淚 think where we are right now is like having seen that seed sprouted. And now we are starting to see a few of its first leaves,鈥 Kim said. 鈥淔or quantum computing to contribute to science and society, it is important that people with different expertise come together. That鈥檚 why we need an institute.鈥
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