Geometry and dynamics of annealed optimization in the coherent Ising machine with hidden and planted solutions

The coherent Ising machine (CIM) is a nonconventional hardware architecture for finding approximate solutions to large-scale combinatorial optimization problems. It operates by annealing a laser gain parameter to adiabatically deform a high-dimensional energy landscape over a set of soft spins, going from a simple convex landscape to the more complex optimization landscape of interest. We address how the evolving energy landscapes guides the optimization dynamics against problems with hidden planted solutions. We study the Sherrington-Kirkpatrick spin-glass with ferromagnetic couplings that favor a hidden configuration by combining the replica method, random matrix theory, the Kac-Rice method and dynamical mean-field theory. We characterize energy, number, location, and Hessian eigenspectra of global minima, local minima, and critical points as the landscape evolves increasing the laser gain. We find that low-energy global minima develop soft modes which the optimization dynamics can exploit to descend the energy landscape. Even when these global minima are aligned to the hidden configuration, there can be exponentially many higher-energy local minima that are all unaligned with the hidden solution. Nevertheless, the annealed optimization dynamics can evade this cloud of unaligned high-energy local minima and descend near to aligned lower-energy global minima. Eventually, as the landscape is further annealed, these global minima become rigid, terminating any further optimization gains from annealing. We further consider a second optimization problem, the Wishart planted ensemble, which contains a hidden planted solution in a landscape with tunable ruggedness. We describe CIM phase transitions between recoverability and nonrecoverability of the hidden solution. Overall, we find intriguing relations between high-dimensional geometry and dynamics in analog machines for combinatorial optimization.