In this paper we present a quantum annealing algorithm for computation of homology of large point clouds. It is designed to work on resizable cloud computing platforms. The algorithm takes as input a witness complex K approximating the given point cloud. It uses quantum annealing to compute a clique cover of the 1-skeleton of K and then uses this cover to blow up K into a Mayer-Vietoris complex. Finally, it computes the homology of the Mayer-Vietoris complex in parallel. The novelty here is the use of clique covering which, compared to graph partitioning, substantially reduces the computational load assigned to each slave machine in the computing cluster. We describe two different clique covers and their quantum annealing formulation in the form of quadratic unconstrained binary optimization (QUBO).