  We consider the distributional connection between the lossy compressed
representation of a high-dimensional signal $X$ using a random spherical code
and the observation of $X$ under an additive white Gaussian noise (AWGN). We
show that the Wasserstein distance between a bitrate-$R$ compressed version of
$X$ and its observation under an AWGN-channel of signal-to-noise ratio
$2^{2R}-1$ is sub-linear in the problem dimension. We utilize this fact to
connect the risk of an estimator based on an AWGN-corrupted version of $X$ to
the risk attained by the same estimator when fed with its bitrate-$R$ quantized
version. We demonstrate the usefulness of this connection by deriving various
novel results for inference problems under compression constraints, including
minimax estimation, sparse regression, compressed sensing, and the universality
of linear estimation in remote source coding.
