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

2025/06/09 by Anteneh, Amanuel, Brunel, Léandre, González-Arciniegas, Carlos +1 · 2 citations
#FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Quantum Physics (quant-ph)

paper · doi:10.48550/arxiv.2506.07859

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.

Citations

• End-to-end switchless architecture for fault-tolerant photonic quantum computing
• Toward coherent quantum computation of scattering amplitudes with a measurement-based photonic quantum processor
• Machine learning for efficient generation of universal hybrid quantum computing resources
• Robust preparation of Wigner-negative states with optimized SNAP-displacement sequences
• Fusion-based quantum computation
• Proximal Policy Optimization Algorithms
• Encoding a qubit in an oscillator
• Reinforcement Learning: An Introduction

Cited by

• Implementing the Koopman-von Neumann approach on continuous-variable photonic quantum computers
• Measurement-induced cubic phase state generation

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