While recent work suggests that quantum computers can speed up the solution of semidefinite programs, little is known about the quantum complexity of more general convex optimization. We present a quantum algorithm that can optimize a convex function over an (n)-dimensional convex body using (\tilde{O}(n)) queries to oracles that evaluate the objective function and determine membership in the convex body. This represents a quadratic improvement over the best-known classical algorithm. We also study limitations on the power of quantum computers for general convex optimization, showing that it requires (\tilde{Ω}(\sqrt n)) evaluation queries and (Ω(\sqrt{n})) membership queries.