Complex complex landscapes - Jaron Kent-Dobias; Jorge Kurchan (Physical Review Research, 2021)
We study the saddle points of the p-spin model—the best understood example of a “complex” (rugged) landscape—when its N variables are complex. These points are the solutions to a system of N random equations of degree p−1. We solve for N[over ¯], the number of solutions averaged over randomness in the N→∞ limit. We find that it saturates the Bézout bound lnN[over ¯]∼Nln(p−1) The Hessian of each saddle is given by a random matrix of the form C^{†}C, where C is a complex symmetric Gaussian matrix with a shift to its diagonal. Its spectrum has a transition where a gap develops that generalizes the notion of “threshold level” well known in the real problem. The results from the real problem are recovered in the limit of real parameters. In this case, only the square root of the total number of solutions are real. In terms of the complex energy, the solutions are divided into sectors where the saddles have different topological properties.