Deep reinforcement learning for near-deterministic preparation of cubic- and quartic-phase gates in photonic quantum computing
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Abstract (arXiv)
Cubic-phase states are a sufficient resource for universal quantum computing over continuous variables. We present results from numerical experiments in which deep neural networks are trained via reinforcement learning to control a quantum optical circuit for generating cubic-phase states, with an average success rate of 96%. The only non-Gaussian resource required is photon-number-resolving measurements. We also show that the exact same resources enable the direct generation of a quartic-phase gate, with no need for a cubic gate decomposition.
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- 2017Proximal Policy Optimization Algorithmscited by 15,215
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