  We explore the impact of coarse quantization on low-rank matrix sensing in
the extreme scenario of dithered one-bit sampling, where the high-resolution
measurements are compared with random time-varying threshold levels. To recover
the low-rank matrix of interest from the highly-quantized collected data, we
offer an enhanced randomized Kaczmarz algorithm that efficiently solves the
emerging highly-overdetermined feasibility problem. Additionally, we provide
theoretical guarantees in terms of the convergence and sample size
requirements. Our numerical results demonstrate the effectiveness of the
proposed methodology.
