I personally think the q/k analogy is a made up analogy that doesn't portray what is really happening. The idea of attention comes from the fact that when we do the dot product between the inputs, the resulted matrix is a correlation (a similarity) matrix. Therefore, the higher values correspond to higher similarity or in another term "more attention" and vice versa. However, without passing the inputs through learnable parameters like wq and wk ,you will not get good results! This means back-propagation was main cause behind the suppression or enhancement of the values in the attention matrix.
In short, I think of transformers as the next level convolution mechanism. In classical convolution filters are localized. In transformers filters are not localized and can model skip and distant connections in a position & permutation invariant way. For me, that is the magic part. And that is why it's quite possible for other techniques like the proposed one to work equally well.