Finding a good approximation of the top eigenvector of a given (d\times d) matrix (A) is a basic and important computational problem, with many applications. We give two different quantum algorithms that, given query access to the entries of a Hermitian matrix (A) and assuming a constant eigenvalue gap, output a classical description of a good approximation of the top eigenvector: one algorithm with time complexity (\mathcal{\tilde{O}}(d^{1.75})) and one with time complexity (d^{1.5+o(1)}) (the first algorithm has a slightly better dependence on the (ℓ₂)-error of the approximating vector than the second, and uses different techniques of independent interest). Both of our quantum algorithms provide a polynomial speed-up over the best-possible classical algorithm, which needs (Ω(d^2)) queries to entries of (A), and hence (Ω(d^2)) time. We extend this to a quantum algorithm that outputs a classical description of the subspace spanned by the top-(q) eigenvectors in time (qd^{1.5+o(1)}). We also prove a nearly-optimal lower bound of (\tilde{Ω}(d^{1.5})) on the quantum query complexity of approximating the top eigenvector. Our quantum algorithms run a version of the classical power method that is robust to certain benign kinds of errors, where we implement each matrix-vector multiplication with small and well-behaved error on a quantum computer, in different ways for the two algorithms. Our first algorithm estimates the matrix-vector product one entry at a time, using a new “Gaussian phase estimation” procedure. Our second algorithm uses block-encoding techniques to compute the matrix-vector product as a quantum state, from which we obtain a classical description by a new time-efficient unbiased pure-state tomography procedure.