Research

We develop theoretical foundations and practical tools to enable useful quantum computing, including noise characterization and mitigation, quantum error correction, and efficient quantum–classical interfaces. We aim to understand how these methods can improve the reliability, efficiency, and capabilities of quantum computation, bridging fundamental theory and practical implementations on both near-term and fault-tolerant quantum devices.

We develop and analyze quantum algorithms, with a particular focus on quantum many-body simulation and computation. Our research includes preparing quantum states, simulating quantum dynamics, and extracting physical information such as energies, spectral properties, and correlation functions. We seek to understand how quantum algorithms can efficiently investigate complex quantum systems and address computational problems that are challenging for classical methods.

We investigate the computational complexity of quantum many-body problems, particularly the Local Hamiltonian problem. Our research combines complexity-theoretic analysis with mathematical tools such as semidefinite programming (SDP) and nonlinear algebra. We aim to understand how the mathematical and physical structures of quantum systems influence their computational complexity and the capabilities and limitations of both quantum and classical algorithms.

We explore the interplay between AI and quantum computing, including both quantum-enhanced AI and AI-enhanced quantum computing. On one hand, we investigate how quantum computing can enhance machine learning and information processing. On the other, we develop AI-driven approaches for designing quantum algorithms and protocols, learning quantum systems, and optimizing quantum computations. Our goal is to understand and advance the complementary capabilities of AI and quantum computing.