We explore the broad question posed in the title from different perspectives. We show how a classical neural network can be trained to optimally embed features into a quantum system and to optimally extract information from it. We review the concept of variational quantum error mitigation, i.e., the idea of variationally optimizing error mitigation strategies. We present recent results demonstrating how classical deep learning models and noisy quantum computers can cooperate to better estimate quantum expectation values. Finally, as a speculative open problem, we propose pushing the core question to its extreme limit: Can AI autonomously decide how to optimally use a noisy quantum computer without hard-coding any specific error-reduction strategy?