Sampling diverse near-optimal solutions via algorithmic quantum annealing

Understand

Sampling a diverse set of high-quality solutions for hard optimization problems is of great practical relevance in many scientific disciplines and applications, such as artificial intelligence and operations research.

• One of the main open problems is the lack of ergodicity, or mode collapse, for typical stochastic solvers based on Monte Carlo techniques leading to poor generalization or lack of robustness to uncertainties.
• Currently, there is no universal metric to quantify such performance deficiencies across various solvers.
• Here, we introduce a new diversity measure for quantifying the number of independent approximate solutions for NP-hard optimization problems.