Autonomous Floquet Engineering of Bosonic Codes via Reinforcement Learning

TL;DR

This paper presents a reinforcement learning-based Floquet engineering method for efficiently and robustly preparing bosonic quantum error-correcting codes, significantly reducing preparation time and enhancing noise resilience.

Contribution

It introduces a novel AI-assisted Floquet control technique for bosonic codes, enabling faster and more robust state preparation compared to traditional methods.

Findings

Achieves over 100x reduction in state preparation time.

Maintains high fidelity under strong noise conditions.

Demonstrates scalability and experimental feasibility.

Abstract

Bosonic codes represent a promising route toward quantum error correction in continuous-variable systems, with direct relevance to experimental platforms such as circuit QED and optomechanics. However, their preparation and stabilization remain highly challenging, requiring ultra-precise control of nonlinear interactions to create entangled superpositions, suppress decoherence, and mitigate dynamic errors. Here, we introduce a reinforcement-learning-assisted Floquet engineering approach for the autonomous preparation of bosonic codes that is general, efficient, and noise-resilient. By leveraging machine learning to optimize Floquet driving parameters, our method achieves over two orders of magnitude reduction in evolution time-requiring only about one percent of that in conventional adiabatic schemes-while maintaining high-fidelity state generation even under strong dissipative and dephasing noise. This approach not only demonstrates the power of artificial intelligence in quantum control but also establishes a scalable and experimentally feasible route toward fault-tolerant bosonic quantum computation. Beyond the specific application to bosonic code preparation, our results suggest a general paradigm for integrating machine learning and Floquet engineering to overcome decoherence challenges in next-generation quantum technologies.