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Quantum information-processing techniques enable work extraction from asystem's inherently quantum features, in addition to the classical free energyit contains. Meanwhile, the science of computational mechanics affords toolsfor the predictive modeling of non-Markovian classical and quantum stochasticprocesses. We combine tools from these two sciences to develop a technique forpredictive work extraction from non-Markovian stochastic processes with quantumoutputs. We demonstrate that this technique can extract more work thannon-predictive quantum work extraction protocols, on one hand, and predictivework extraction without quantum information processing, on the other. Wediscover a phase transition in the efficacy of memory for work extraction fromquantum processes, which is without classical precedent. Our work opens up theprospect of machines that harness environmental free energy in an essentiallyquantum, essentially time-varying form.

Engines for predictive work extraction from memoryful quantum stochastic processes