Researchers have demonstrated that fault-tolerant quantum computing could potentially provide efficient solutions for large-scale machine learning models. Their work shows that quantum algorithms could help overcome the computational, power, and time constraints of traditional machine-learning models. They also suggest that quantum enhancement is possible in the early stages of learning after model pruning. This research indicates that quantum algorithms could contribute significantly to solving large-scale machine-learning problems.
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