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Nature0 Hot0 comments3 min read
Autonomous Floquet engineering of bosonic codes via reinforcement learning
Bosonic codes represent a promising route toward quantum error correction in continuous-variable systems, with direct relevance to experimental platforms. However, their preparation and stabilization remain challenging, requiring ultra-precise control of nonlinear interactions to create entangled superpositions, suppress decoherence, and mitigate dynamic errors. Here, we present a Floquet engineering framework in which reinforcement learning is used to realize the autonomous design of the control waveforms for bosonic code preparation, yielding a general, efficient and noise-resilient strategy. By leveraging reinforcement 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 reasonably strong dissipative and dephasing noise. This approach establishes a scalable, experimentally feasible route for using machine learning and Floquet engineering for quantum control in fault-tolerant bosonic quantum computation, and provides a general framework for using machine learning to mitigate decoherence in quantum technologies. Fast and reliable preparation of error-correcting bosonic states is a key challenge for fault-tolerant quantum computing. By combining reinforcement learning with Floquet engineering, the authors develop an autonomous control framework that accelerates bosonic code generation while preserving resilience against realistic quantum noise.
Source: https://www.nature.com/articles/s42005-026-02875-2. 8 Sync News only summarizes and links out; content copyright belongs to the authors and original sources.
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