Mixed integer Linear Program (MILP), i.e., with no quadratic constraints, is already in general NP hard, even without quadratic constraints.
If the problem had only continuous variables, it could be converted to a MILP via the Karush Kuhn Tucker conditions, which requires introduction of binary variables to handle the complementarity constraints occurring in the KKT conditions.
If the problem had only some mixture of binary and general integer variables, then with introduction of a large number of variables and constraints, it could be converted to a MILP.
However, quadratic terms in which both variables in the term are continuous can not be "linearized" via introduction of binary variables and additional constraints. The presence also of binary or general integer variables in the problem renders the KKT conditions inapplicable. Therefore, there is no MILP formulation, with any number of variables or constraints, into which the mixed quadratic problem can be transformed.
If you want to effectively solve this problem, don't aspire to convert it into a MILP, rather, solve it with a branch and bound mixed integer solver which can natively handle quadratic constraints. If the objective is linear or convex quadratic, and C is symmetric positive semidefinite, then you can solve with MIQCQP (Mixed Integer Quadraticaly Constrained Quadratic Program) or MISOCP (Mixed Integer Second Order Cone Problem) solvers, such as CPLEX, GUROBI, or MOSEK