Compressed Sensing (CS) is a sampling paradigm used for acquiring sparse or compressible signals from a seemingly incomplete set of measurements. In any practical application with our digitally driven technology, these "compressive measurements" are quantized and thus they do not have infinite precision. So far, the theory of quantization in CS has mainly focused on compressive sampling systems designed with random measurement matrices. In this note, we turn our attention to "deterministic compressed sensing". Specifically, we focus on quantization in CS with chirp sensing matrices and present quantization approaches and numerical experiments.