报告人简介：

Sanjiang Li is a full professor at the Centre for Quantum Software & Information (QSI) at the University of Technology Sydney (UTS), Australia. He received his B.Sc. and PhD in mathematics from Shaanxi Normal University and Sichuan University in 1996 and 2001, respectively. Before joining UTS, he worked at Tsinghua University from 2001 to 2008. He was an Alexander von Humboldt research fellow at Freiburg University and held prestigious positions such as a Microsoft Research Asia Young Professorship and an ARC Future Fellowship.

His early research focused on knowledge representation and artificial intelligence, particularly in spatial knowledge representation and reasoning. Recently, his work has expanded into quantum circuit compilation and verification, as well as quantum AI.

Professor Li's research has been published in leading journals and conferences, including Artificial Intelligence, IEEE TC, IEEE TCAD, ACM TODAES, and AAAI, IJCAI, DAC, ICCAD.

讲座简介：

Quantum state preparation (QSP) is a fundamental task in quantum computing and quantum information processing. It is critical to the execution of many quantum algorithms, including those in quantum machine learning. In this paper, we propose a family of efficient QSP algorithms tailored to different numbers of available ancilla qubits — ranging from no ancilla qubits, to a single ancilla qubit, to a sufficiently large number of ancilla qubits. Our approach exploits the power of Local Invertible Map Tensor Decision Diagrams (LimTDDs) — a highly compact representation of quantum states that combines tensor networks and decision diagrams to reduce quantum circuit complexity. Extensive experiments demonstrate that our methods significantly outperform existing approaches and exhibit better scalability for large-scale quantum states, both in terms of runtime and gate complexity. Furthermore, our method shows exponential improvement in best-case scenarios.