Intelligent Optimal Control of Rydberg Gates with Incremental-Update Deep Reinforcement Learning

TL;DR

This paper presents a DRL-based framework for high-fidelity, fast Rydberg controlled-NOT gates that autonomously optimize pulse profiles, outperforming traditional methods in quantum computing.

Contribution

It introduces an incremental-update learning policy for autonomous, efficient quantum gate control without prior heuristics, achieving high fidelity and speed.

Findings

Peak average fidelity of 0.9991 achieved

Framework reduces computational overhead

Autonomously discovers early-cutoff policy

Abstract

Deep reinforcement learning (DRL), acting as a novel and powerful paradigm for quantum optimal control, offers transformative opportunities for advancing neutral-atom quantum computing. In this work, we theoretically demonstrate a DRL-based framework for realizing Rydberg controlled-NOT gates that achieve both high speed and high fidelity through the synchronous modulation of multiple pulse parameters without any prior heuristic ansatz. By introducing an incremental-update learning policy, our framework effectively regularizes the exploration of the control landscape, ensuring the generation of smooth, experimentally feasible pulse profiles while significantly reducing computational overhead compared to conventional schemes. Crucially, the framework autonomously discovers an early-cutoff policy by optimally reconciling operation speed with high-precision coherent control. Our optimized protocol achieves a peak average fidelity of 0.9991, significantly outperforming conventional methods and surpassing the critical fault-tolerant threshold. This work establishes a generalizable, AI-driven pathway for designing high-performance quantum gates and provides a robust paradigm for autonomous control field optimization across diverse qubit platforms.