用Transformer实现量子反馈控制，突破传统方法局限。
Quantum feedback control with a transformer neural network architecture
- 采用Transformer架构捕捉长时序关联，提升控制效率。
- 在非理想测量与扰动下仍达近似完美态保真度。
- 适合量子纠错、实时调控及噪声环境下的快速控制。
基于注意力机制的神经网络（如Transformer）已在自然语言处理、基因组学和视觉等领域带来变革。本文首次将Transformer应用于量子反馈控制，通过监督学习和强化学习两种方式实现。得益于其捕捉长时序相关性和训练高效性的优势，该方法超越了以往基于循环神经网络或策略型强化学习的控制方案。以双态系统为目标，在存在非理想测量与哈密顿量扰动（未包含在训练集中）的情况下，所设计的专用Transformer架构可在短时间内实现接近单位保真度的状态稳定。此外，该模型在强化学习任务中还可实现非可积多体量子系统的能量最小化。本方法可用于量子纠错、有色噪声环境下的快速量子态控制，以及量子器件的实时调校与表征。
原文摘要 · Abstract (English)
Attention-based neural networks such as transformers have revolutionized various fields such as natural language processing, genomics, and vision. Here, we demonstrate the use of transformers for quantum feedback control through both a supervised and reinforcement learning approach. In particular, due to the transformer's ability to capture long-range temporal correlations and training efficiency, we show that it can surpass some of the limitations of previous control approaches, e.g.~those based on recurrent neural networks trained using a similar approach or policy based reinforcement learning. We numerically show, for the example of state stabilization of a two-level system, that our bespoke transformer architecture can achieve near unit fidelity to a target state in a short time even in the presence of inefficient measurement and Hamiltonian perturbations that were not included in the training set as well as the control of non-Markovian systems. We also demonstrate that our transformer can perform energy minimization of non-integrable many-body quantum systems when trained for reinforcement learning tasks. Our approach can be used for quantum error correction, fast control of quantum states in the presence of colored noise, as well as real-time tuning, and characterization of quantum devices.
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