Machine learning for efficient generation of universal hybrid quantum computing resources
arXiv:2310.03130 · doi:10.1364/OPTICAQ.523445
Abstract
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.
arXiv comment10 pages, 8 figures, submitted
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