Gaussian Boson Sampling is a variant of the well known Boson Sampling, for which the input is Gaussian states (squeezed vacuum and/or coherent states) instead of single photons. Same as the Boson Sampling, Gaussian Boson Sampling is proposed to demonstrate quantum supremacy in a near-term quantum device. It is tempting to find potential applications of Gaussian Boson Sampling. In this talk, I will discuss the connection between Gaussian Boson Sampling and graph theory, and its potential applications to graph related problems. In particular, I will discuss in detail how to use a Gaussian Boson Sampling device to solve the graph isomorphism problem and the graph similarity problem.
报告人简介：粟待钦，博士，2005年到2012年在中国科大天体物理专业学习，获得学士和硕士学位。硕士期间研究方向为宇宙学和引力波。 2013年赴澳大利亚昆士兰大学，师从Timothy Ralph教授，从事量子信息和相对论的研究。主要方向为利用量子信息理论来研究匀加速系和引力场中的量子力学效应。 2017年博士毕业后加入位于加拿大多伦多的初创公司Xanadu，从事连续变量光学量子计算的研究。主要研究方向为量子计算架构，量子算法和量子纠错。