报告人：许振朋

单位：安徽大学

报告时间：2026年7月9日 星期四 10:00

报告地点：知新楼C1011

邀请人：于晓东

报告内容：Optimal strategies for shadow tomography with limited resources

报告摘要：Shadow tomography addresses the task of efficiently predicting many expectation values of an unknown quantum state from randomized measurements on comparatively few copies. Existing analyses promise large scaling advantages, but the optimal strategies realizing these guarantees are not always known, and the required measurements are potentially challenging  to implement on current hardware. We address this gap for Pauli observables by computing optimal sample-complexity parameters and constructing optimal measurement strategies under realistic resource constraints. We focus on memoryless protocols, where each copy is measured only once, and on measurements with bounded interaction range. Our approach reduces the problem to the analysis of graph parameters of the frustration graph encoding Pauli anticommutation, using the theory of $\hbar$-perfect graphs developed in a companion work. We prove that Clifford measurements are optimal in many situations. This includes $\hbar$-perfect frustration graphs and all single-qubit and two-qubit measurement scenarios. Applied to Hamiltonian energy estimation, our framework yields constructive strategies and improved variance bounds for molecular benchmarks.

报告人简介：许振朋，安徽大学yl1111永利集团教授，博士毕业于南开大学陈省身数学研究所，之后在德国锡根大学从事博士后工作，获德国洪堡基金会支持。研究方向为量子力学基础问题和量子信息，专注于不同系统中量子关联的刻画与应用。迄今已在量子信息理论基础领域发表SCI论文四十余篇，其中第一／通讯作者（含共同）Physical Review Letters 5篇，Nature Communications、Science Advances各1篇。研究工作获2021年度奥地利科学院颁发的埃伦费斯特量子基础最佳论文奖，天津市自然科学一等奖。入选海外博后引才专项，安徽省百人计划。