We present numerical simulations of deep reinforcement learning on a measurement-based quantum processor--a time-multiplexed optical circuit sampled by photon-number-resolving detection--and find it generates squeezed cat states with an average success rate of 98%, far outperforming all other similar proposals. Since squeezed cat states are deterministic precursors to the Gottesman-Kitaev-Preskill bosonic error code, this is a key result for enabling fault tolerant photonic quantum computing.
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