A survey on HHL algorithm: From theory to application in quantum machine learning

The Harrow-Hassidim-Lloyd (HHL) algorithm is a method to solve the quantum linear system of equations that may be found at the core of various scientific applications and quantum machine learning models including the linear regression, support vector machines and recommender systems etc. After reviewing the necessary background on elementary quantum algorithms, we provide detailed account of how HHL is exploited in different quantum machine learning (QML) models, and how it provides the desired quantum speedup in all these models. At the end, we briefly discuss some of the remaining challenges ahead for HHL-based QML models and related methods.     

降钙素基因相关肽衍生物的制备及降血压功能研究

用PCR法将人的降钙素基因相关肽(calcitonin gene related—peptide,CGRP)基因克隆到表达载体pGEX-4T-2的GST基因后,构建成一个新的表达质粒pGEX—CGRP．另外分别将2个有降压功能的小肽HHL和RPLKPW的基因克隆到CGRP基因后,构建成2个表达质粒pGEX—CGRP-3AA和pGEX—CGRP-6AA．将此3个表达质粒转化到大肠杆菌TG1中,获得3个高表达的融合蛋白：GST—CGRP,GST—CGRP—HHL,GST—CGRP—RPLKPW;通过G1utathione Sepharose 4B亲和层析纯化融合蛋白,测定蛋白浓度,利用自发性高血压大鼠(spontaneously hypertensive rat,SHR)来检测蛋白的降血压功能,发现融合蛋白GST—CGRP—HHL具有显著的降压效果,能使舒张压下降8．0kPa,且降压维持时间延长,达到70min以上．