This is a very interesting question, which actually asks about the interplay between equidistribution (or harmonic analysis if you would like to call it that way) and ergodic theory.

As Vaughn mentioned, for any $L^{p}$ function ($p\geq 1$), the pointwise ergodic theorem would imply that for Lebesgue almost every point $x$, the value of the ergdoic averages $$ \frac{1}{N}\sum_{n=0}^{N-1}f(x+n \alpha) \to \int fd\mu$$ where $\mu$ is the Lebesgue measure. Hence one can take the characteristic function of your favourite measurable set, and get the "equidistribution" statement you would like, but only at the cost of convergence a.e.

Noam showed why a.e. is sharp here.

When one asks about continuous function (or more frequently in the field, takes a function from a suitable Sobolev space), one can get more information than the statement above (including for example estimation about the errors), see for example this inequality for your problem - http://en.wikipedia.org/wiki/Low-discrepancy_sequence , but this also comes at a cost, you usually have to limit the irrational number your dealing with to be Diophantine generic or so.

As you may know, Weyl's equidistribution was generalized to equidistribution of sequences of the form ${p(n)\alpha}$, where $p(n)$ is some polynomial in $Z[x]$. Now one can ask about such "ergodic limits" for the sequence ${n^{2}\alpha}$, for general $L^{p}$ function (continuous functions will be dealt easily by the equidistribution theorem). It was an open question for some time, but the main result here is by Bourgain, which showed that for any $p>1$, if the function is in $L^{p}$, then the averages will converge a.e. to the integral. This is the analogue of the usual pointwise ergodic theorem for the quadratic case. The interesting point here is that $p>1$ is sharp, there is today a counterexample for $p=1$ case.

This is a very interesting question, which actually asks about the interplay between equidistribution (or harmonic analysis if you would like to call it that way) and ergodic theory.

As Vaughn mentioned, for any $L^{p}$ function ($p\geq 1$), the pointwise ergodic theorem would imply that for Lebesgue almost every point $x$, the value of the ergdoic averages $$ \frac{1}{N}\sum_{n=0}^{N-1}f(x+n \alpha) \to \int fd\mu$$ where $\mu$ is the Lebesgue measure. Hence one can take the characteristic function of your favourite measurable set, and get the "equidistribution" statement you would like, but only at the cost of convergence a.e.

Noam showed why a.e. is sharp here.

When one asks about continuous function (or more frequently in the field, takes a function from a suitable Sobolev space), one can get more information than the statement above (including for example estimation about the errors), see for example this inequality for your problem - https://en.wikipedia.org/wiki/Low-discrepancy_sequence , but this also comes at a cost, you usually have to limit the irrational number your dealing with to be Diophantine generic or so.

As you may know, Weyl's equidistribution was generalized to equidistribution of sequences of the form ${p(n)\alpha}$, where $p(n)$ is some polynomial in $Z[x]$. Now one can ask about such "ergodic limits" for the sequence ${n^{2}\alpha}$, for general $L^{p}$ function (continuous functions will be dealt easily by the equidistribution theorem). It was an open question for some time, but the main result here is by Bourgain, which showed that for any $p>1$, if the function is in $L^{p}$, then the averages will converge a.e. to the integral. This is the analogue of the usual pointwise ergodic theorem for the quadratic case. The interesting point here is that $p>1$ is sharp, there is today a counterexample for $p=1$ case.

This is a very interesting question, which actually asks about the interplay between equidistribution (or harmonic analysis if you would like to call it that way) and ergodic theory.

As Vaughn mentioned, for any L^{p} function (p>=1), the pointwise ergodic theorem would imply that for Lebesgue almost every point $x$, the value of the ergdoic averages $$ \frac{1}{N}\sum_{n=0}^{N-1}f(x+n \alpha) \to \int fd\mu$$ where $mu$ is the Lebesgue measure. Hence one can take the characteristic function of your favourite measurable set, and get the "equidistribution" statement you would like, but only at the cost of convergence a.e.

Noam showed why a.e. is sharp here.

When one asks about continuous function (or more frequently in the field, takes a function from a suitable Sobolev space), one can get more information than the statement above (including for example estimation about the errors), see for example this inequality for your problem - http://en.wikipedia.org/wiki/Low-discrepancy_sequence , but this also comes at a cost, you usually have to limit the irrational number your dealing with to be Diophantine generic or so.

As you may know, Weyl's equidistribution was generalized to equidistribution of sequences of the form ${p(n)\alpha}$, where p(n) is some polynomial in $Z[x]$. Now one can ask about such "ergodic limits" for the sequence ${n^{2}\alpha}$, for general L^{p} function (continuous functions will be dealt easily by the equidistribution theorem). It was an open question for some time, but the main result here is by Bourgain, which showed that for any $p>1$, if the function is in $L^{p}$, then the averages will converge a.e. to the integral. This is the analogue of the usual pointwise ergodic theorem for the quadratic case. The interesting point here is that $p>1$ is sharp, there is today a counterexample for $p=1$ case.

This is a very interesting question, which actually asks about the interplay between equidistribution (or harmonic analysis if you would like to call it that way) and ergodic theory.

As Vaughn mentioned, for any $L^{p}$ function ($p\geq 1$), the pointwise ergodic theorem would imply that for Lebesgue almost every point $x$, the value of the ergdoic averages $$ \frac{1}{N}\sum_{n=0}^{N-1}f(x+n \alpha) \to \int fd\mu$$ where $\mu$ is the Lebesgue measure. Hence one can take the characteristic function of your favourite measurable set, and get the "equidistribution" statement you would like, but only at the cost of convergence a.e.

Noam showed why a.e. is sharp here.

When one asks about continuous function (or more frequently in the field, takes a function from a suitable Sobolev space), one can get more information than the statement above (including for example estimation about the errors), see for example this inequality for your problem - http://en.wikipedia.org/wiki/Low-discrepancy_sequence , but this also comes at a cost, you usually have to limit the irrational number your dealing with to be Diophantine generic or so.

As you may know, Weyl's equidistribution was generalized to equidistribution of sequences of the form ${p(n)\alpha}$, where $p(n)$ is some polynomial in $Z[x]$. Now one can ask about such "ergodic limits" for the sequence ${n^{2}\alpha}$, for general $L^{p}$ function (continuous functions will be dealt easily by the equidistribution theorem). It was an open question for some time, but the main result here is by Bourgain, which showed that for any $p>1$, if the function is in $L^{p}$, then the averages will converge a.e. to the integral. This is the analogue of the usual pointwise ergodic theorem for the quadratic case. The interesting point here is that $p>1$ is sharp, there is today a counterexample for $p=1$ case.