This is a set of lecture notes for a Ph.D.-level course on quantum algorithms, with an emphasis on quantum optimization algorithms. It is developed for applied mathematicians and engineers, and requires no previous background in quantum mechanics. The main topics of this course, in addition to a rigorous introduction to the computational model, are: input/output models, quantum search, the quantum gradient algorithm, matrix manipulation algorithms, the mirror descent framework for semidefinite optimization (including the matrix multiplicative weights update algorithm), adiabatic optimization.

翻译：这是一套面向博士研究生水平的量子算法课程讲义，重点聚焦于量子优化算法。本讲义专为应用数学家和工程师设计，无需预先具备量子力学背景。除对计算模型进行严格介绍外，本课程主要涵盖以下主题：输入/输出模型、量子搜索、量子梯度算法、矩阵处理算法、半定优化的镜像下降框架（包括矩阵乘性权重更新算法）、绝热优化。