We develop quantum algorithms for simulating strongly-interacting physical systems and studying molecular structures. This includes applications to manybody physics, high-energy physics, and quantum chemistry. Additionally, we explore ways to use classical computing to make quantum computing more efficient, including using neural network and tensor network methods.
Our researchJoin the groupReal-time and phase-diagram studies of manybody physics and chemistry on quantum hardware.
Development of hybrid classical-quantum methods along with ML-assisted techniques for efficient quantum computing. Applications include optimization, quantum sensing, and quantum circuit generation.
Relativistic quantum information, chaos, and the quantum nature of spacetime.