Bryan K Clark

Bryan K Clark is an Associate Professor in the Department of Physics at the University of Illinois, specializing in Computational Condensed Matter Physics and Quantum Computing. He leads the Clark Research Group focused on developing computational tools to understand many-body physics and advance quantum computing capabilities.

His research spans the intersection of quantum information, condensed matter physics, machine learning, and computing. Clark's group develops computational methods including neural network backflow approaches, quantum Monte Carlo techniques, tensor networks, and machine learning algorithms for quantum systems. His work addresses fundamental questions in quantum many-body physics, quantum algorithms, and the quantum-classical boundary.

His recent publications (2024-2025) demonstrate expertise across quantum simulation, quantum error mitigation, neural network quantum states, and applications of machine learning to physics problems. These works show a strong trend toward integrating machine learning with quantum computing and developing novel approaches for quantum state preparation and characterization.

• Ranked Excellent Teacher by students multiple times (Spring 2024, Spring 2022, Spring 2020, Fall 2018, Fall 2017)
• Recipient of DOE grants for quantum information science research
• Secured NSF funding for machine learning applications in nano-photonics manufacturing
• IBM funding as part of the IIDAI institute for shadow tomography and error mitigation

Clark actively advises PhD students, with recent successful defenses by Faisal Alam, Matt Thibodeau, Chad Germany, James Allen, and Abid. His pedagogical contributions include developing computational physics curriculum at Illinois, notably Physics 246 (Intro to Computational Physics) and Physics 446 (Modern Computational Physics), where students build quantum computing simulators, implement numerical renormalization, and explore connections between Ising models and machine learning.