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Entanglement is a valuable resource for learning, yet precisely characterizing its advantage can be challenging. We demonstrate that quantum entanglement can provide an exponential advantage in learning properties of both discrete-variable and continuous-variable quantum systems. Additionally, we analyze the robustness of entanglement-assisted quantum learning protocols in the presence of practical imperfections. Our work elucidates the role of entanglement in quantum learning and suggests experimentally feasible demonstrations of provable entanglement-enabled advantages using various quantum platforms.