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

arXiv: https://arxiv.org/abs/2506.07859 · HTML: https://mlsanity.com/paper/2506.07859/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 53 references that are in the map.
- First posted on arXiv 9 Jun 2025.

## Builds on (most cited first, 1 of 1)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)

## 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.