Configuration barrier towards parity-time symmetry in randomly connected mesoscopic sets on a graph★
Laboratoire Charles Fabry, Institut d’Optique Graduate School, CNRS, Univ. Paris Saclay,
2 Av. Augustin Fresnel,
Palaiseau Cedex, France
2 Université de Paris, LIED, CNRS UMR 8236, 5 Rue Thomas Mann, 75013 Paris, France
a e-mail: email@example.com
Received in final form: 31 August 2020
Accepted: 1 September 2020
Published online: 12 October 2020
We address the issue of dissipative vs. non-dissipative behavior in a mesoscopic set of coupled elements such as oscillators, with one half having gain and the other half having losses. We introduce a graph with coupling as the graph edges in given fixed number and gain/loss elements as its nodes. This relates to parity-time symmetry, notably in optics, e.g. set of coupled fibers, and more generally to the issue of taming divergence related to imaginary parts of eigenvectors in various network descriptions, for instance biochemical, neuronal, ecological. We thus look for the minimization of the imaginary part of all eigenvalues altogether, with a collective figure of merit. As more edges than gain/loss pairs are introduced, the unbroken cases , i.e., stable cases with real eigenvalues in spite of gain and loss, become statistically very scarce. A minimization from a random starting point by moving one edge at a time is studied, amounting to investigate how the hugely growing configuration number impedes the attainment of the desired minimally-dissipative target. The minimization path and its apparent stalling point are analyzed in terms of network connectivity metrics. We expand in the end on the relevance in biochemical signaling networks and the so-called “stability-optimized circuits” relevant to neural organization.
Key words: Mesoscopic and Nanoscale Systems
Supplementary material in the form of one pdf file available from the Journal web page at https://doi.org/10.1140/epjb/e2020-10219-x.
© EDP Sciences / Società Italiana di Fisica / Springer-Verlag GmbH Germany, part of Springer Nature, 2020