By harnessing the power of machine learning to analyze data produced by experiments into electron behavior, physicist in the College of Arts and Sciences (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 “Accelerating Machine-Learning-Driven Discovery in Quantum Materials.” 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.
The complicated characteristics of electrons that make them difficult to understand also make them powerful in developing valuable materials and computing tools – the way each has an inherent spin, for example, or the way two or more electrons can share a seemingly telepathic “entangled” bond. But experiments into these and other qualities of quantum materials produce particularly difficult data sets, which machine learning can help with, said Kim, professor of physics in A&S.
Machine learning is also changing Kim’s 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.
“One of the promising aspects of quantum computing is that you can handle a much larger volume of information when you use qubits,” 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’s spin – 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’s been encoded in qubits proves difficult: “Although there is a tantalizing possibility of being able to encode a lot of information, reading it out is a non-trivial process with qubits.”
Even with all these recent and future developments, quantum computing is at the seedling stage, Kim said. Just as the first inventors of classical computing could not imagine the iPhone, researchers working on quantum condensed matter physics can’t imagine what applications might lead to, she said.
With , associate professor of applied and engineering physics (Engineering), and Alumni Affairs and 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 – quantum theory, quantum experiment and quantum technology.
“I 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,” Kim said. “For quantum computing to contribute to science and society, it is important that people with different expertise come together. That’s why we need an institute.”
