But I’m not completely satisfied with this, because it does lack the component that computation or learning in ML do seem to capture something fundamental happening in the physical world.
Why do I think that this question is of similar importance to the ones that you’re mentioning in your post? Because knowing what kind of knowledge a field of research produces, and what it can be useful for, is fundamental to using the fruits of this research.
It seems to me that computer science is ontologically prior to physics, but not by as much as mathematics, in kinda the same way that statistical mechanics and thermodynamics are (but maybe a little further up the chain of abstaction). The laws of thermodynamics hold in a large class of possible universes with a wide range of possible physical laws, but very far from all possible universes with all possible laws. If they study something physical, maybe it is something about constraints on the general mathematical character of physical law in our universe; that seems to be how the implications cash out for quantum information theory, too.
It seems to me that computer science is ontologically prior to physics, but not by as much as mathematics, in kinda the same way that statistical mechanics and thermodynamics are (but maybe a little further up the chain of abstaction). The laws of thermodynamics hold in a large class of possible universes with a wide range of possible physical laws, but very far from all possible universes with all possible laws. If they study something physical, maybe it is something about constraints on the general mathematical character of physical law in our universe; that seems to be how the implications cash out for quantum information theory, too.