基于类扩散模型的量子误差缓解
Quantum Error Mitigation with Diffusion-Like Models
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中文总结 AI 辅助
该研究针对量子系统退相干问题，提出基于类扩散模型的AI辅助误差缓解框架，通过机器学习学习去噪映射，在单量子比特、多量子比特寄存器上验证，可兼容噪声分布式量子系统，用于近似非幺正动力学与缓解相干损失。
中文摘要 AI 辅助
量子系统与环境之间的耦合会通过将信息从系统转移到环境自由度而导致退相干。当在时间上离散化时，这类相互作用可被解释为弱测量序列，该序列为噪声量子动力学提供了有效模型。受此图像启发，我们提出了一种AI辅助的量子扩散过程误差缓解框架，该过程由顺序局域弱测量产生。正向过程通过在随机选择的泡利基中进行的弱测量逐步擦除输入态的信息，平均而言产生依赖于基的局域退相和局域去极化动力学。机器学习模型在精确合成密度矩阵上进行训练，以学习特定于信道和分布的去噪映射并估计对应的噪声前态。我们在单量子比特态以及可分离和纠缠的多量子比特寄存器上对该方法进行基准测试。我们还研究了依赖于分布的局域到全局重构，其中局域约化密度矩阵被用于重构全局态。这种受实验启发的设置依赖于可局域访问的信息，因此与噪声和分布式量子系统兼容。更广泛地说，该框架提供了一种混合经典-量子方法，用于近似非幺正动力学并缓解相干损失。
英文摘要
Coupling between a quantum system and its environment causes decoherence by transferring information from the system to environmental degrees of freedom. When discretized in time, such interactions can be interpreted as sequences of weak measurements that provide an effective model of noisy quantum dynamics. Motivated by this picture, we propose an AI-assisted error-mitigation framework for quantum diffusion processes generated by sequential local weak measurements. The forward process progressively erases information from the input state through weak measurements performed in randomly selected Pauli bases, producing basis-dependent local dephasing and locally depolarizing dynamics on average. Machine-learning models are trained on exact synthetic density matrices to learn a channel- and distribution-specific denoising map and estimate the corresponding pre-noise state. We benchmark the approach on single-qubit states and separable and entangled multi-qubit registers. We also study distribution-dependent local-to-global reconstruction, in which local reduced density matrices are used to reconstruct the global state. This experimentally motivated setting relies on locally accessible information and is therefore compatible with noisy and distributed quantum systems. More broadly, the framework provides a hybrid classical-quantum approach for approximating non-unitary dynamics and mitigating coherence loss.
