  This note studies the worst-case recovery error of low-rank and bisparse
matrices as a function of the number of one-bit measurements used to acquire
them. First, by way of the concept of consistency width, precise estimates are
given on how fast the recovery error can in theory decay. Next, an idealized
recovery method is proved to reach the fourth-root of the optimal decay rate
for Gaussian sensing schemes. This idealized method being impractical, an
implementable recovery algorithm is finally proposed in the context of
factorized Gaussian sensing schemes. It is shown to provide a recovery error
decaying as the sixth-root of the optimal rate.
