A new $2 million National Science Foundation award is funding University of Maryland-led research to develop a new approach to quantum computing that uses machine learning concepts to bypass roadblocks to building practical quantum computers.
Full-scale quantum computers that would rely on conventional gate-based quantum computer architectures require quantum error correction—a goal outside the reach of existing technology. In this project, the researchers will focus on architectures inspired by machine learning and deep learning to implement quantum protocols that are naturally efficient and robust against noise. The project will leverage quantum photonics, based on manipulation of quantum states of light, to develop these quantum machine learning architectures.
The project, jointly funded by the NSF’s Quantum Leap Big Idea Program and the Division of Electrical, Communications, and Cyber Systems in the Directorate for Engineering, seeks to advance applications in quantum simulation, machine learning, optimization, and quantum communication.
The collaborative research is led by electrical and computer engineering Professor Edo Waks, a fellow of the Joint Quantum Institute and the Quantum Technology Center. He is working with computer science Professor Andrew Childs, co-director of the Joint Center for Quantum Information and Computer Science and a fellow of UMD’s Institute for Advanced Computer Studies, as well as with professors Seth Lloyd and Dirk Englund of Massachusetts Institute of Technology.
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