This thesis deals with the study of quantum networks with a complex structure, the implications this structure has in the distribution of entanglement and how their functioning can be enhanced by operating in the quantum regime. We first consider a complex network of bipartite states, both pure and mixed, and study the distribution of long-distance entanglement. Then, we move to a network with noisy channels and study the creation and distribution of large, multipartite states.
The work contained in this thesis is primarily motivated by the idea that the interplay between quantum information and complex networks may give rise to a new understanding and characterization of natural systems. Complex networks are of particular importance in communication infrastructures, as most present telecommunication networks have a complex structure. In the case of quantum networks, which are the necessary framework for distributed quantum processing and for quantum communication, it is very plausible that in the future they acquire a complex topology resembling that of existing networks, or even that methods will be developed to use current infrastructures in the quantum regime.
A central task in quantum networks is to devise strategies to distribute entanglement among its nodes. In the first part of this thesis, we consider the distribution of bipartite entanglement as an entanglement percolation process in a complex network. Within this approach, perfect entanglement is established probabilistically between two arbitrary nodes. We see that for large networks, the probability of doing so is a constant strictly greater than zero (and independent of the size of the network) if the initial amount of entanglement is above a certain critical value. Quantum mechanics offer here the possibility to change the structure of the network without need to establish new, "physical" channels. By a proper local transformation of the network, the critical entanglement can be decreased and the probability increased. We apply this transformation to complex network models with arbitrary degree distribution.
In the case of a noisy network of mixed states, we see that for some classes of states, the same approach of entanglement percolation can be used. For general mixed states, we consider a limited-path-length entanglement percolation constrained by the amount of noise in the connections. We see how complex networks still offer a great advantage in the probability of connecting two nodes.
In the second part, we move to the multipartite scenario. We study the creation and distribution of graph states with a structure that mimic the underlying communication network. In this case, we use an arbitrary complex network of noisy channels, and consider that operations and measurements are also noisy. We propose an efficient scheme to distribute and purify small subgraphs, which are then merged to reproduce the desired state. We compare this approach with two bipartite protocols that rely on a central station and full knowledge of the network structure. We show that the fidelity of the generated graphs can be written as the partition function of a classical disordered spin system (a spin glass), and its decay rate is the analog of the free energy. Applying the three protocols to a one-dimensional network and to complex networks, we see that they are all comparable, and in some cases the proposed subgraph protocol, which needs only local information of the network, performs even better.