Binary representation of data and algorithmic complexity Question: What is known about the role that binary representation of data plays in algorithmic complexity theory?

I define "analog representation of data" loosely as any method of representing data which is not a sequence of 1's and 0.  A more precise definition would be nice to have obviously.  The basic idea is that an analog representation of data contains additional information beyond what is contained in a binary representation.
A very trivial example of an "analog" representation of the natural numbers is to label all the primes $p_1, p_2, p_3 \cdots$ and then represent numbers by their prime factorization, instead of in binary form.  It is obvious that with this representation of numbers, the problem of factoring large numbers is of polynomial complexity.
Less trivially, according the Shor's Alogirithm, if we represent numbers using an analog physical system (in this case, using quibits instead of bits), then it's possible to factor large numbers in polynomial time as well.
The natural conclusion to draw is that the choice to use binary or not for representing data likely plays an important role in determining if a given problem is in P or NP, and one would think that in order to prove P is not equal to NP it would be required to use the assumption that data is represented in a binary way at some point.
Sorry for the open ended question.  Any and all feedback is very much appreciated.  I am getting out of my comfort zone here and was unable to find much help using google.
Thanks!
 A: "Binary or not" is not the central issue. Well designed data structures can speed up classical computations, but underneath those data structures it's all bits. What quantum computing does is to allow for massively parallel computation, thus problems that may be NP on a classical computer can be P on a quantum computer.  
Here's the start of wikipedia's discussion:

Quantum computing studies theoretical computation systems (quantum
  computers) that make direct use of quantum-mechanical phenomena, such
  as superposition and entanglement, to perform operations on data.1
  Quantum computers are different from binary digital electronic
  computers based on transistors. Whereas common digital computing
  requires that the data are encoded into binary digits (bits), each of
  which is always in one of two definite states (0 or 1), quantum
  computation is analog and uses quantum bits, which can be in an
  infinite number of superpositions of states.

A: The term "analog", in data communication, refers to continuous functions of time, as opposed to a discrete-valued one. Using that term for representing the integers by their prime factorizations conflicts with the practice. 
If you are trying to ask whether we lose information (accuracy) by going from the uncountable (the reals) to a countable subset, then for all practical purposes we do not: the rationals are dense in the reals. 
Further, I don't think the polynomial time achievement in Schor's algorithm is in how we represent numbers. It's in the quantum parallelism.
