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RetroMPA boosts retrosynthesis prediction accuracy by injecting chemical knowledge into existing models, achieving significant improvements without the need for retraining.
Mixed-state prototypes allow quantum models to learn new classes without expanding circuit complexity, achieving robust performance with fewer qubits.