用超网络让量子态重构模型一次训练，通用于整个相图。
Parametric Quantum State Tomography with HyperRBMs
- 用超网络让RBM根据哈密顿参数动态调整，实现参数化量子态重建。
- 在1D/2D伊辛模型上，从局域测量中高保真重建跨相变区的量子态。
- 无需已知临界点即可识别相变，并准确复现保真度易感性。
量子态层析（QST）对验证量子设备至关重要，但随系统规模呈指数增长。神经网络量子态（如受限玻尔兹曼机，RBM）可高效参数化多体量子态，已成功用于QST。然而，现有方法为逐点式，需在相图每个参数值重新训练。本文提出基于超网络的参数化QST框架，将RBM条件于哈密顿控制参数，使单一模型可表示整个量子基态族。应用于横场伊辛模型，在1D和2D晶格上，仅通过局部泡利测量即实现跨两相及临界区的高保真重建。关键成果是模型无需先验知识即可准确复现保真度易感性并识别量子相变。结果表明，超网络调制的神经量子态为全相图的层析重建提供了高效且可扩展的路径。
原文摘要 · Abstract (English)
Quantum state tomography (QST) is essential for validating quantum devices but suffers from exponential scaling in system size. Neural-network quantum states, such as Restricted Boltzmann Machines (RBMs), can efficiently parameterize individual many-body quantum states and have been successfully used for QST. However, existing approaches are point-wise and require retraining at every parameter value in a phase diagram. We introduce a parametric QST framework based on a hypernetwork that conditions an RBM on Hamiltonian control parameters, enabling a single model to represent an entire family of quantum ground states. Applied to the transverse-field Ising model, our HyperRBM achieves high-fidelity reconstructions from local Pauli measurements on 1D and 2D lattices across both phases and through the critical region. Crucially, the model accurately reproduces the fidelity susceptibility and identifies the quantum phase transition without prior knowledge of the critical point. These results demonstrate that hypernetwork-modulated neural quantum states provide an efficient and scalable route to tomographic reconstruction across full phase diagrams.
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