  In this paper, we compare and catalog the performance of various greedy
quantized compressed sensing algorithms that reconstruct sparse signals from
quantized compressed measurements. We also introduce two new greedy approaches
for reconstruction: Quantized Compressed Sampling Matching Pursuit (QCoSaMP)
and Adaptive Outlier Pursuit for Quantized Iterative Hard Thresholding
(AOP-QIHT). We compare the performance of greedy quantized compressed sensing
algorithms for a given bit-depth, sparsity, and noise level.
