There is much hype surrounding quantum computing and its potential applications for optimization. However, the technical details are often lost in translation. This talk provides an overview of quantum algorithms that could potentially be useful for continuous or discrete optimization. Most of the discussion will be devoted to the benefits and limitations of algorithms for SDP and LP via faster solution of linear systems, or with the multiplicative weights update method. We will also discuss some fundamental open questions, highlighting what algorithmic limitations need to be overcome for quantum computing to have an impact on the practice of optimization.