报告题目：On constructing benchmark quantum circuits with known near-optimal transformation cost

报告人：澳大利亚悉尼科技大学Sanjiang Li教授

报告时间：2022年11月25日（周五）上午10:00

报告地点：腾讯会议（会议号：748-166-097，密码：1896）

主持人：龙治国博士

报告摘要：

       Current quantum devices impose strict connectivity constraints on quantum circuits, making circuit transformation necessary before running logical circuits on real quantum devices. Many quantum circuit transformation (QCT) algorithms have been proposed in the past several years.  This work proposes a novel method for constructing benchmark circuits and uses these benchmark circuits to evaluate state-of-the-art QCT algorithms, including t|ket⟩ from Cambridge Quantum Computing, Qiskit from IBM, and three academic algorithms SABRE, SAHS, and MCTS. These benchmarks have known near-optimal transformation costs and thus are called QUEKNO (for quantum examples with known near-optimality). Compared with QUEKO benchmarks designed by Tan and Cong (2021), which all have zero optimal transformation costs, QUEKNO benchmarks are more general and can provide a more faithful evaluation for QCT algorithms (like t|ket⟩) which use subgraph isomorphism to find the initial mapping. Our evaluation results show that SABRE can generate transformations with conspicuously low average costs on the 53-qubit IBM Q Rochester and Google’s Sycamore in both gate size and depth objectives. (Joint work with Xiangzhen Zhou and Yuan Feng.)

报告人简介：

       Sanjiang Li received his B.Sc. and PhD in mathematics from Shaanxi Normal University in 1996 and Sichuan University in 2001. He is now a full professor in the Centre of Quantum Software & Information (QSI), Faculty of Engineering & Information Technology, University of Technology Sydney (UTS), Australia. Before joining UTS, he worked in the Computer Science and Technology Department, Tsinghua University, from September 2001 to December 2008. He was an Alexander von Humboldt research fellow at Freiburg University from January 2005 to June 2006; held a Microsoft Research Asia Young Professorship from July 2006 to June 2009; and an ARC Future Fellowship from January 2010 to December 2013.

       His research interests are mainly in knowledge representation and artificial intelligence. The main objective of his previous research was to establish expressive representation formalism of spatial knowledge and provide effective reasoning mechanisms. Recently, he is also interested in research in quantum artificial intelligence. The aim is to develop quantum algorithms for solving AI problems and apply AI methods to solve classical problems in quantum computing. 

       Some of his most important work has been published in international journals like Artificial Intelligence, IEEE TC, IEEE TCAD, ACM TODAES and international conferences like IJCAI, AAAI, KR, DAC, ICCAD.