Deep reinforcement learning for near-deterministic preparation of cubic-phase gates and direct preparation of quartic-phase gates in photonic quantum computing
Abstract
Cubic-phase states are a sufficient gate 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 by leveraging quantum interference in phase space, with an unprecedented 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.
Fields of Study & Subjects
Neural Networks and Reservoir Computing
Quantum Computing Algorithms and Architecture
Quantum Information and Cryptography
Publication details
- Journal / source
- Physical Review Research
- Volume
- 8
- Issue
- 1
- Publication date
- 10 February 2026
- Language
- EN