Machine learning for efficient generation of universal hybrid quantum computing resources

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.