发表机构
Chalmers University of Technology; University of Gothenburg（查尔姆斯理工大学; 哥德堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于仿真的无似然量子系统推断框架，利用经典模拟器与神经密度估计器直接学习参数后验，实现高效、可复用的推断，并在多种量子任务中验证其准确性与实用性。
AI 中文摘要
量子系统的模型能够忠实地将系统参数映射到观测结果，但从测量数据中推断参数的逆问题面临根本性挑战：由于希尔伯特空间呈指数级扩大，似然函数在计算上难以处理。在此，我们引入基于仿真的量子系统推断，这是一种统一的、无似然框架，直接从经典仿真数据中学习参数后验分布。核心思想是将多项式成本的经典模拟器（如泡利传播和张量网络）与归一化流或其他神经密度估计器配对，以实现准确且可复用的推断。一个经过一次性训练的模型，能够在一次前向传播中将任何新的测量记录映射到其后验分布——将每次实验的推断转化为固定的前期成本。我们通过数值实验展示了该框架在泡利噪声学习、量子误差缓解、量子态层析和哈密顿量学习中的广泛适用性，示例涉及81量子比特浅层电路和735参数推断。在每种情况下，该方法都能对可识别参数给出准确估计，而后验不确定性则提供了额外的非可识别性诊断，并指示需要进一步表征的方面。我们的框架降低了量子实验中的数据采集需求，加速了参数推断，为表征和改进大规模量子系统提供了一条实用途径。
英文摘要
Models of quantum systems faithfully map system parameters to observations, but the inverse problem of parameter inference from measurement data presents a fundamental challenge: computationally intractable likelihoods due to an exponentially large Hilbert space. Here, we introduce simulation-based quantum system inference, a unified, likelihood-free framework that learns parameter posteriors directly from classical simulation data. The central idea is to pair polynomial-cost classical simulators, such as Pauli propagation and tensor networks, with normalizing flows or other neural density estimators for accurate, reusable inference. A single model, trained once, maps any new measurement record to its posterior in one forward pass---turning per-experiment inference into a fixed, up-front cost. We numerically demonstrate the framework's versatility across Pauli noise learning, quantum error mitigation, quantum state tomography, and Hamiltonian learning, with examples involving 81-qubit shallow circuits and 735-parameter inference. In each case, the approach yields accurate estimates of identifiable parameters, while posterior uncertainty provides additional diagnostics of non-identifiability and indicates where further characterization is needed. Our framework reduces data-acquisition requirements in quantum experiments and accelerates parameter inference, providing a practical route to characterizing and improving large-scale quantum systems.
Comments24 pages; 15 figures
