  Quadratic optimization problems (QPs) are ubiquitous, and solution algorithms
have matured to a reliable technology. However, the precision of solutions is
usually limited due to the underlying floating-point operations. This may cause
inconveniences when solutions are used for rigorous reasoning. We contribute on
three levels to overcome this issue. First, we present a novel refinement
algorithm to solve QPs to arbitrary precision. It iteratively solves refined
QPs, assuming a floating-point QP solver oracle. We prove linear convergence of
residuals and primal errors. Second, we provide an efficient implementation,
based on SoPlex and qpOASES that is publicly available in source code. Third,
we give precise reference solutions for the Maros and M\'esz\'aros benchmark
library.
