# Junyu Liu

Dr. Junyu Liu is a computer scientist who will join Pitt as an assistant professor of computer science in 2024 Fall. In the past, he worked as a theoretical physicist affiliated with the University of Chicago and IBM. 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.

Dr. Liu has a keen interest in the combination of physics and computing, especially machine learning, quantum computing, and quantum networks. His work encompasses areas such as quantum machine learning, variational quantum circuits, quantum optimization, and quantum data centers. 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.

1. Representation learning via quantum neural tangent kernels J Liu, F Tacchino, JR Glick, L Jiang, A Mezzacapo PRX Quantum 3, 030323 2. Analytic theory for the dynamics of wide quantum neural networks J Liu, K Najafi, K Sharma, F Tacchino, L Jiang, A Mezzacapo Physical Review Letters, 130, 150601 (2023) 3. Chaos, complexity, and random matrices J Cotler, N Hunter-Jones, J Liu, B Yoshida Journal of High Energy Physics 2017 (11), 1-60 4. Quantum Simulation for High Energy Physics CW Bauer, Z Davoudi, AB Balantekin, T Bhattacharya, M Carena, ... PRX Quantum 4, 027001 5. Collisions of false-vacuum bubble walls in a quantum spin chain A Milsted, J Liu, J Preskill, G Vidal PRX Quantum 3, 020316

1. Hybrid Quantum-Classical Scheduling for Accelerating Neural Network Training with Newton's Gradient Descent P Li, J Liu, H Wang, T Chen arXiv preprint arXiv:2405.00252 2. A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance Y Wang, J Liu arXiv preprint arXiv:2401.11351 3. Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions Y Alexeev, M Amsler, P Baity, MA Barroca, S Bassini, T Battelle, D Camps, ... arXiv preprint arXiv:2312.09733 4. Dynamical phase transition in quantum neural networks with large depth B Zhang, J Liu, XC Wu, L Jiang, Q Zhuang arXiv preprint arXiv:2311.18144 5. Towards real-world implementations of quantum machine learning J Liu Quantum Views 7, 77