On the complexity of quantum link prediction in complex networks

Abstract

Link prediction methods use patterns in known network data to infer which connections may be missing. Previous work has shown that continuous-time quantum walks can be used to represent path-based link prediction, which we further study here to develop a more optimized quantum algorithm. Using a sampling framework for link prediction, we analyze the query access to the input network required to produce a certain number of prediction samples. Considering both well-known classical path-based algorithms using powers of the adjacency matrix as well as our proposed quantum algorithm for path-based link prediction, we argue that there is a polynomial quantum advantage on the dependence on N, the number of nodes in the network. We further argue that the complexity of our algorithm, although sub-linear in N, is limited by the complexity of performing a quantum simulation of the network’s adjacency matrix, which may prove to be an important problem in the development of quantum algorithms for network science in general.

Complex quantum networks: a topical review

“…On a related note, one may consider the complexity of simulating the CTQW itself on a universal quantum computer. It is the case that common algorithms become inefficient in complex networks with hubs [181], however an algorithm for simulating hub sparse networks has been recently proposed [182] as a step towards exploring whether quantum computers can have an advantage in simulating dynamics on complex networks.…”