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Learning the properties of dynamical quantum systems underlies applications ranging from nuclear magnetic resonance spectroscopy to quantum device characterization. A central challenge in this pursuit is the learning of strongly interacting systems, where conventional observables decay quickly in time and space, limiting the information that can be learned from their measurement. In this work, we introduce a new class of observables into the context of quantum learning—the out-of-time-order correlator—which we show can substantially improve the learnability of strongly interacting systems by virtue of displaying informative physics at large times and distances. We identify two general scenarios in which out-of-time-order correlators provide a significant learning advantage:(i) when experimental access to the system is spatially restricted, for example, via a single “probe” degree of freedom, and (ii) when one …