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AlphaClifford consistently outperforms state-of-the-art synthesis heuristics by reducing gate counts while using a less expressive gate set, revolutionizing Clifford circuit optimization.
Model-based reinforcement learning slashes CNOT gate count in quantum circuits by up to 32%, outperforming heuristic and model-free RL methods.
