Xiao Yuan's research focuses on quantum information science, dedicated to addressing the core challenge of making quantum computing practical. His work systematically connects the theoretical design, application exploration, and experimental implementation of quantum algorithms, spanning interdisciplinary fields such as quantum chemistry, quantum materials, and quantum artificial intelligence.
Facing the limitations of near-term quantum hardware, Xiao Yuan has proposed and developed various efficient quantum algorithms. His variational quantum algorithm significantly reduces circuit depth by dynamically constructing shallow quantum circuits, while his virtual ruantum resource distillation theoretical framework provides new approaches for effectively utilizing quantum resources on noisy devices.
On the application front, he combines quantum embedding methods with quantum computing to simulate the electronic structure of strongly correlated materials. He also extends this approach to molecular docking problems, providing a quantum solution for drug discovery. These studies effectively reduce the number of qubits required for complex problems, showcasing the potential of near-term quantum computers for industrial applications.
To advance the physical realization of his theoretical proposals, Xiao Yuan collaborates closely with experimental teams. Together, they have performed large-scale quantum chemistry simulations on a superconducting quantum computing platform, improving computational accuracy by approximately two orders of magnitude through hardware optimization and error mitigation. They also prepared and verified large cluster states up to 51 qubits, laying the foundation for building intermediate-scale quantum computers.
