  Algorithmic discrepancy theory seeks efficient algorithms to find those
two-colorings of a set that minimize a given measure of coloring imbalance in
the set, its {\it discrepancy}. The {\it Euclidean discrepancy} problem and the
problem of balancing covariates in randomized trials have efficient randomized
algorithms based on the Gram-Schmidt walk (GSW). We frame these problems as
quantum Ising models, for which variational quantum algorithms (VQA) are
particularly useful. Simulating an example of covariate-balancing on an IBM
quantum simulator, we find that the variational quantum eigensolver (VQE) and
the quantum approximate optimization algorithm (QAOA) yield results comparable
to the GSW algorithm.
