Community detection in quantum complex networks (Physical Review X)

Real-life networks such as groups of animals and biochemical assemblies exhibit complex relationships that can benefit from systematic study. The macroscopic properties of a network cannot be easily deduced from knowledge of its microscopic properties. Such a deduction is aided by the identification of strongly connected subnetworks, called communities. Despite a growing interest in large networks in quantum biology, transport and communication, no methods are currently known for community detection in quantum networks.

In a new paper published by Physical Review X and co-authored by ISI Research Leader Jacob Biamonte and ISI Researcher Mauro Faccin, scientists extend the concept of community detection from classical to quantum systems, providing a crucial missing tool for analyzing quantum systems with a network structure.

In Community detection in quantum complex networks, authors argue that breaking down a quantum system into strongly correlated parts, i.e., a form of community partitioning, is an essential precursor for any simulation that aims to use this partitioning to reduce computational costs. By investigating quantum systems that are generally smaller than the classical systems typically studied, researchers were able to detect better subcommittees then a partitioning done by hand in the quantum chemistry literature.

Read the full .pdf at Physical Review X.