No! This is a huge misconception that misses the essence of quantum mechanics.
The Uncertainty is not due to finite information. It is an inherent property!
Uncertainty is due to (or better, "modeled by", because FT is math, not physics, and you could use wavelets or something instead), Fourier (and similar transforms) is a (bidirectional) function that maps (local) points to (dispersed) waves, and vice versa. This holds true even with infinite precision/information.
"Qualitatively, this means a narrow function has a wide Fourier transform, and a wide function has a narrow Fourier transform. In either domain, a wider function means there is literally a wide distribution of data, so there always exists uncertainty in one domain."
The discrete Fourier transform (which is what OP is talking about) doesn't satisfy an uncertainty principle as far as I know. The concept doesn't really make sense.
You can talk about these concepts without bringing information or precision into the picture.
Discrete FT is just an approximate of the the continuous FT, so it follows the same uncertainty principle. It doesn't have an additional uncertainty feature, jt just has the same approximation error that any finite model of an infinite phenomenon would have. That's not "uncertainty" because you can get arbitrarily precision by adding more samples. But Uncertainty means that it it impossible to get below the Uncertainty threshold, just like how if you squeeze a (theoretical) balloon full of incompressible fluid, you can never reduce its global volume to zero, even though you can reduce any local part of it to zero volume.
I'm happy to be wrong and learn about an uncertainty principle for the DFT. I'm just not aware of one. Your comment isn't very helpful for illuminating, absence any mathematical detail.
I've never heard of one either.
The first time I was introduced to an uncertainty principle for the DFT was in time-frequency analysis which exists due to not being able to know both time domain and frequency domain information simultaneously. There is some shared concepts from QM, but they are not the same thing.
That a non periodic signal eg something which is nonzero only in a short span, requires a infinite number of periodic functions i.e fourier series/transform to represent is better considered as a limitation of the basis, it simply cannot represent such, only approximate it by a signal with a infinite period, which is not the same as non-periodic to mathematicians.
The definition of the signal implies its expression both in fourier and spatial basis exactly, with no uncertainty.
For this to have an interpretation as uncertainty requires that the functions themselves have that interpretation.
> The discrete Fourier transform doesn't satisfy an uncertainty principle as far as I know. The concept doesn't really make sense.
It certainly does. There are different formulations of it (by Donoho-Stark and by Tao, that I know of). They work when the domain is a finite abelian group.
I guess the original article [0] is a good starting reference. A post [1] in Tao's blog gives a nice overview.
If you just want a short statement of the discrete uncertainty principle: If you define the discrete Fourier transform F(u) for functions u defined on a finite abelian group G, and you denote by |S(u)| the cardinal of the support of u, then you have the inequalities:
|S(u)| · |S(F(u))| >= |G|
and (as consequence)
|S(u)| + |S(F(u))| >= 2 sqrt(|G|)
Notice that this contains as a particular case the discrete Fourier transform, where the abelian group in question is the integers modulo N.
This has a very practical and intutive interpretation: if the signal u is very localized, then its spectrum F(u) cannot be very localized at the same time, for their supports must be large enough (with respect to the total size of the domain).
[0] Donoho D. L. and Stark P. B., Uncertainty Principles and Signal Recovery, SIAM Journal of Applied Mathematics, 49 (1989), 906–931
> The Uncertainty is not due to finite information. It is an inherent property!
That's what they said, though? Although I suppose it could stand to be emphasized more:
Physical quantities that have an uncertainty principle, such as position and momentum, are inherently related to each other by a Fourier transform. At a quantum-mechanical level, position is the Fourier transform of momentum, and vice versa - anything that constrains the position to a narrow interval (including but not limited to measuring it very precisely) inherently also smears momentum over a wide range of possibilities, even it was itself previously tighly constained (and, again, vice versa).
Apologies if you consider this pedantic, but I prefer to phrase it in terms of the wavefunctions in position and momentum, rather than position and momentum, unadorned:
At a quantum-mechanical level, the wavefunction in the position domain is the Fourier transform of the wavefunction in the momentum domain, and vice versa - anything that constrains the position wavefunction to a narrow interval inherently also smears the momentum wavefunction...
Again, apologies if this seems pedantic. I know enough about quantum mechanics to be dangerous, but am no expert, so pedantic wording helps me keep things straight.
Honestly, if anything I'd say the pedatry goes the other way: pedantically, the wavefunction in the position domain is what a particle's position is (and respectively for momentum); it's making a distiction between the two (or pretending that there's anything in the position domain other than wave functions) that's insufficiently pedantic.
Uncertainty is due to (or better, "modeled by", because FT is math, not physics, and you could use wavelets or something instead), Fourier (and similar transforms) is a (bidirectional) function that maps (local) points to (dispersed) waves, and vice versa. This holds true even with infinite precision/information.
http://math.uchicago.edu/~may/REU2021/REUPapers/Dubey.pdf
"Qualitatively, this means a narrow function has a wide Fourier transform, and a wide function has a narrow Fourier transform. In either domain, a wider function means there is literally a wide distribution of data, so there always exists uncertainty in one domain."