  Spin glasses are notoriously difficult to study both analytically and
numerically due to the presence of frustration and metastability. Their highly
non-convex landscapes require collective updates to explore efficiently.
Currently, most state-of-the-art algorithms rely on stochastic spin clusters to
perform non-local updates, but such "cluster algorithms" lack general
efficiency. Here, we introduce a non-equilibrium approach for simulating spin
glasses based on classical dynamics with memory. By simulating various classes
of 3d spin glasses (Edwards-Anderson, partially-frustrated, and
fully-frustrated models), we find that memory dynamically promotes critical
spin clusters during time evolution, in a self-organizing manner. This
facilitates an efficient exploration of the low-temperature phases of spin
glasses.
