Random Quantum Networks

Josh Dees, Antoine Jacquier, Sylvain Laizet

Abstract Classical Physics-informed neural networks (PINNs) approximate solutions to PDEs with the help of deep neural networks trained to satisfy the differential operator and the relevant boundary conditions. We revisit this idea in the quantum computing realm, using parameterised random quantum circuits as trial solutions. We further adapt recent PINN-based techniques to our quantum setting, in particular Gaussian smoothing. Our analysis concentrates on the Poisson, the Heat and the Hamilton–Jacobi–Bellman equations, which are ubiquitous in most areas of science. On the theoretical side, we develop a complexity analysis of this approach, and show numerically that random quantum networks can outperform more traditional quantum networks as well as random classical networks.

Sébastien Perseguers, Maciej Lewenstein, Antonio Acín +1 more

Shuangshuang Fu, Guodong Shi, Ian R. Petersen +1 more

We investigate the evolution of the network entropy for consensus dynamics in classical and quantum networks. We show that in the classical case, the network differential entropy is monotonically non-increasing if the node initial values are continuous random variables. While for quantum consensus dynamics, the network's von Neumann entropy is in contrast non-decreasing. In light of this inconsistency, we compare several distributed algorithms with random or deterministic coefficients for classical or quantum networks, and show that quantum algorithms with deterministic coefficients are physically related to classical algorithms with random coefficients.

Günter Mahler A, Volker A. Weberruß

Günter Mahler A, Volker A. Weberruß

Jaroslav Novotný, G. Alber, Igor Jex

Networks constitute efficient tools for assessing universal features of complex systems. In physical contexts, classical as well as quantum networks are used to describe a wide range of phenomena, such as phase transitions, intricate aspects of many-body quantum systems, or even characteristic features of a future quantum internet. Random quantum networks and their associated directed graphs are employed for capturing statistically dominant features of complex quantum systems. Here, we develop an efficient iterative method capable of evaluating the probability of a graph being strongly connected. It is proven that random directed graphs with constant edge-establishing probability are typically strongly connected, i.e., any ordered pair of vertices is connected by a directed path. This typical topological property of directed random graphs is exploited to demonstrate universal features of the asymptotic evolution of large random qubit networks. These results are independent of our knowledge of the details of the network topology. These findings suggest that other highly complex networks, such as a future quantum internet, may also exhibit similar universal properties.