Deep reinforcement learning for near-deterministic preparation of cubic- and quartic-phase gates in photonic quantum computing
This work addresses the challenge of near-deterministic gate preparation in photonic quantum computing, which is incremental but offers practical improvements for quantum optics researchers.
The authors tackled the problem of generating cubic-phase states and quartic-phase gates for photonic quantum computing using deep reinforcement learning, achieving an average success rate of 96% for cubic-phase states and enabling direct quartic-phase gate generation without decomposition.
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.