Deep reinforcement learning for near-deterministic preparation of cubic- and quartic-phase gates in photonic quantum computing

Amanuel Anteneh, Léandre Brunel, Carlos González-Arciniegas, Olivier Pfister
6/9/2025
quant-phcs.LG

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Abstract

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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Cite this paper

@article{anteneh2025deep,
  title  = {Deep reinforcement learning for near-deterministic preparation of cubic- and quartic-phase gates in photonic quantum computing},
  author = {Amanuel Anteneh and Léandre Brunel and Carlos González-Arciniegas and Olivier Pfister},
  year   = {2025},
  eprint = {2506.07859},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2506.07859v2}
}

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