Distributed Graph Isomorphism using Quantum Walks

Graph isomorphism being an NP problem, most of the systems that solves the graph isomorphism are constrained with some classes of the graph, and do not work for all types of graphs in polynomial time. We exploited the two particle quantum walks on different classes of graphs including strongly regular graphs which are co-spectral in nature. We simulated two particle quantum walks on graph using distributed algorithm. To show the effectiveness of the technique, we applied it to the large graphs derived from images using Delauney triangulation. The results show a remarkable speedup for large data. The two-particle quantum walks is implemented in map-reduce programming technique which scales the computation as the cluster get scaled to account Big data. We checked the isomorphism of the graphs with upto 100 vertices in polynomial time. The system is scalable to accept big inputs from any other domain in graph format. DOI: 10.17762/ijritcc2321-8169.15021