Can you efficiently solve a system of quadratic multivariate polynomials?

There is an exact algorithm that needs $n^{O(m)}$ operations (cf. http://arxiv.org/abs/cs/0403008). One cannot expect anything better than that, unless P=NP. Indeed, it is easy to formulate several NP-complete problems as testing solvability of linear equations in 0-1 variables $x_i$, and the latter can be enforced by quadratic equations $x^2_i-x_i=0$.

The quadratic case is as complex as the general case (up to a polynomial-time reduction). Given a set of polynomials $\{p_1,\dots,p_m\}$, you can express each $p_i$ by a straight-line program with instructions of the form $x_i:=c$ ($c\in\mathbb R$ a constant), $x_i:=x_j+x_k$, and $x_i:=x_j\cdot x_k$, which you can in turn translate back to quadratic polynomials (e.g., the last mentioned instruction becomes $x_i-x_jx_k$). The number of variables in the new system will increase (it is linear in the size of the original system), which suggests that you might get an efficient solution if you additionally restrict the number of variables by a constant.

There have been written many books and articles on this subject (see for example the book "Sturmfels, Bernd, Solving systems of polynomial equations. American Mathematical Society, Providence, RI, 2002").
It is not the fact that the polynomials are quadratic which helps, but rather other restrictions which will sometimes result in a more effective solution. If the Groebner basis of the corresponding ideal in $K[x_1,\ldots ,x_n]$ can be computed, and has triangular form, then we can usually solve the system. However, Groebner bases have large exponential complexity and cannot solve in practice systems with, say, more than $15$ variables.
For some overdetermined systems there are other techniques than Groebner bases. One of these is called "relinearization". The exact complexity of this algorithm is not known, but for sufficiently overdetermined systems it is expected to run in polynomial time (see papers by Nicolas Courtois; Alexander Klimov, Jacques Patarin, and Adi Shamir).