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University of Melbourne
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Adversarial attacks on quantum classifiers become exponentially more costly as model complexity increases, revealing a built-in defense mechanism against gradient-based strategies.
Quantum reinforcement learning gets a distributed boost, achieving 10% better performance in multi-agent environments by distributing the learning load across multiple quantum agents.
