Junyu Liu

Junyu Liu is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh’s School of Computing and Information. He joined from the University of Chicago and California Institute of Technology (Caltech), where he conducted postdoctoral research as an IBM fellow at the Chicago Quantum Exchange. His research integrates quantum physics and computer science, focusing on quantum computing systems, machine learning, cybersecurity, and quantum technologies. Key areas include quantum data centers, quantum error correction, post-quantum cryptography, and quantum networks. Dr. Liu holds a PhD in Physics from Caltech and has been recognized with awards such as the IEEE QCE 1st Place Best Paper Award (2023), Kadanoff Fellowship (2021), and Fermilab’s Quantum Information Science Award (2020-2021).

Education: PhD in Physics from Caltech (2021), with postdoctoral training at the Walter Burke Institute for Theoretical Physics and the Institute for Quantum Information and Matter. His interdisciplinary work spans quantum computing, machine learning, and fundamental physics. He is affiliated with the QSAI (Quantum Secure Artificial Intelligence) research group at Pitt, exploring practical applications of quantum technologies.

Research Interests: Quantum machine learning, variational quantum circuits, quantum optimization, quantum data centers, post-quantum cryptography, and quantum communication networks. His work bridges theoretical advancements with real-world implementations, including quantum-classical hybrid systems and quantum security protocols.

Scientific Awards:

• IEEE QCE 1st Place Best Paper Award (2023)
• Kadanoff Fellowship, University of Chicago (2021)
• Quantum Information Science Award, Fermilab (2020-2021)

Advising & Grants: Actively hiring students (undergraduate/graduate) and postdocs for research in quantum computing and AI. His lab offers full RA/TA support. Collaborations include institutions like the Free University of Berlin (Jens Eisert) and the Kadanoff Center for Theoretical Physics. He serves on conference committees (e.g., IEEE QCE, QCNC) and is an Associated Editor of Nature’s NPJ Quantum Information.

Labs/Teams: Leads the QSAI group, which focuses on quantum-secure AI and quantum computing systems. The group aims to advance technologies benefiting academia and industry, including quantum machine learning and fault-tolerant quantum algorithms.