Dr. Junyu Liu is a theoretical physicist working in Liang's group as an IBM postdoc fellow in the Chicago Quantum Exchange. He earned his PhD in Physics from the California Institute of Technology in June 2021, where he gained experience at the Walter Burke Institute for Theoretical Physics and the Institute for Quantum Information and Matter. Junyu has a keen interest in the combination of physics and computing, especially machine learning and other modern computing technologies. His work encompasses areas such as quantum machine learning, variational quantum circuits, quantum optimization, quantum networks, and quantum sensing. His research, published in leading journals and conferences like Physics Review Letters, Nature Communications, Physics Review X Quantum, ICLR, and IEEE, has garnered significant attention in both academia and industry. Dr. Liu has been honored with several awards, including the IEEE QCE 1st place best paper award in quantum algorithms (2023), the Kadanoff Fellowship at the University of Chicago (2021), and the Quantum Information Science Award from Fermilab (2020-2021).
Junyu's research is primarily related to quantum algorithms, with applications in machine learning, optimization, and cryptography. In Liang's group, he co-developed the theory of quantum neural tangent kernel for quantum machine learning, the theory of quantum data centers, the theory of joint quantum-classical networks, and efficient quantum algorithms for machine learning applications.