  In the compressive phase retrieval problem, or phaseless compressed sensing,
or compressed sensing from intensity only measurements, the goal is to
reconstruct a sparse or approximately $k$-sparse vector $x \in \mathbb{R}^n$
given access to $y= |\Phi x|$, where $|v|$ denotes the vector obtained from
taking the absolute value of $v\in\mathbb{R}^n$ coordinate-wise. In this paper
we present sublinear-time algorithms for different variants of the compressive
phase retrieval problem which are akin to the variants considered for the
classical compressive sensing problem in theoretical computer science. Our
algorithms use pure combinatorial techniques and near-optimal number of
measurements.
