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.

报告简介：

Quantum circuit optimisation is a key step toward the efficient execution of quantum algorithms. Template matching has emerged as one of the dominant approaches for simplifying quantum circuits, yet it confronts the intrinsic challenge of accommodating gate commutativity. The state-of-the-art template-matching algorithm relies on a directed acyclic graph (DAG) representation. While this DAG-based technique handles gate commutativity satisfactorily, it fails to preserve the local connectivity inherent to quantum circuits, thereby leading to relatively high matching complexity.

In this paper, we introduce a hypergraph representation (HG), a commutativity-aware representation that collapses commuting gates into a single super-node while retaining all local connectivity. This enables matches to be extended locally without revisiting the rest of the circuit, with incremental updates limited to the immediate neighbourhood. Experimental results demonstrate that our HG matcher achieves 11--606x speed-up over the DAG-based implementation in Qiskit (Iten et al., 2022) on both random and arithmetic benchmarks, while maintaining the same optimisation quality. The acceleration increases with circuit size, confirming that preserving locality and connectivity is the key to scalable quantum circuit optimisation.