Don,
"There is an interesting assumption in information theory - that there is a limit to what can be compressed or represented by a 'unit' of information. There might be a limit, given today's mathematics, but will that always be the case?
How efficiently can I represent pi?"
There are two branches to information theory:
(1) Shannon's original theory has to do with how many discrete (quantized) samples are needed to perfectly reconstruct an arbitrary continuous function, such as those which might be solutions to the equations of mathematical physics. Shannon's Capacity Theorem specifies both the number of required samples and the number of required bits per sample, required to achieve perfect reconstruction. Thus, it provides the missing-link between the the worlds of continuous functions and quantized results. It is easy to show, for example, that setting Shannon's Capacity equal to a single-bit-of-information, will yield the minimum value of the Heisenberg Uncertainty principle. In other words, the Heisenberg Uncertainty Principle simply means that all observations must contain one or more bits of information. Otherwise, it is not an observation at all - just noise. That is why you cannot determine the values of two variables like position and momentum - in the limit, they only encode a single bit of information, between the two variables! This is also the cause of the so called "spooky action at a distance" and the correlations observed in experiemnts attempting to test Bell's Inequality Theorem.
(2) Algorithmic Information Theory, which deals with data compression, AFTER the data has been represents in quantized form.
The physics community has been mostly interested in (2), which is very unfortunate, since it has little relevance to physics, since it deals only with already quantized observations. But (1) addresses the question - Why are observations and measurements quatizable in the first place? - which is of direct relevance to the correct interpretation of quantum theory.
Rob McEachern