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Reinforcement learning can synthesize quantum circuits for state preparation, but struggles to scale beyond a few qubits, suggesting that alternative approaches are needed for larger quantum systems.
Parameter sharing in quantum circuits, while boosting expressivity, can backfire by creating deceptive gradient landscapes that derail classical optimizers.