用深度强化学习控制光路，高效生成量子计算所需相位态
Deep reinforcement learning for near-deterministic preparation of cubic- and quartic-phase gates in photonic quantum computing
- 用深度强化学习优化光量子电路参数
- 生成立方相态成功率高达96%
- 同一方法可直接实现四次相位门
立方相态是连续变量量子计算的通用资源。我们通过数值实验展示，利用深度神经网络结合强化学习训练，可有效控制量子光学电路以生成立方相态，平均成功率达96%。唯一需要的非高斯资源是光子数分辨测量。此外，相同资源还可直接生成四次相位门，无需通过立方相门分解。
原文摘要 · Abstract (English)
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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