Quantum entanglement, or "spooky action at a distance," could help clear a path toward quantum machine learning (ML), say researchers at Los Alamos National Laboratory and Louisiana State University, by overcoming the no-free-lunch theorem, which posits that any ML algorithm is only as good as any other when their performance is averaged over many problems and training datasets.
This implies that modeling a quantum system could require a volume of training data that must grow exponentially as the modeled system expands.
From IEEE Spectrum
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