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

TL;DR

This paper demonstrates using deep reinforcement learning to control quantum optical circuits for efficiently generating cubic- and quartic-phase states, crucial resources for universal quantum computing in continuous variables.

Contribution

It introduces a neural network-based control method that achieves high success rates for preparing cubic- and quartic-phase states without complex decompositions.

Findings

Achieved 96% success rate in generating cubic-phase states.

Enabled direct quartic-phase gate generation with existing resources.

Utilized photon-number-resolving measurements as the key non-Gaussian resource.

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.