通过可观测重建揭示二维费米-哈伯德模型中的物理冗余
Revealing Physical Redundancy in the Two-dimensional Fermi-Hubbard Model via Transferable Observable Reconstruction
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中文总结 AI 辅助
本研究以二维费米-哈伯德模型为对象，通过神经网络重建框架证实不同可观测值间存在可转移的物理信息，为该系统的可观测值级物理冗余提供了数值证据。
中文摘要 AI 辅助
费米-哈伯德模型是研究强关联量子物质的范例体系，不同可观测值常被用于探测电荷、相互作用与自旋关联。本研究探讨这些可观测值是否存在超出表面区分的可相互转移的物理信息，通过二维费米-哈伯德模型的三类代表性可观测值（总密度N、双占据数D、自旋-自旋关联S）间的可转移性测试量化该物理冗余。采用神经网络重建框架，发现某一可观测值的相图可由另一可观测值重建，精度接近自重建基准，尤其在平凡相区；该可转移性依赖正确的物理标记，在有限温区仍保持，对噪声输入也具鲁棒性。研究结果表明，不同可观测值可携带彼此的大量物理信息，为二维费米-哈伯德系统中存在可观测值级的冗余提供了数值证据。
英文摘要
The Fermi-Hubbard model provides a paradigmatic setting for studying strongly correlated quantum matter, where different observables are commonly used to probe charge, interaction, and spin correlations. In this work, we investigate whether these observables contain mutually transferable physical information beyond their apparent distinction. We quantify such physical redundancy through transferability tests among three representative observables of the two-dimensional Fermi-Hubbard model: total density (N), double occupancy (D), and spin-spin correlation (S). Using a neural-network reconstruction framework, we find that the phase diagram of one observable can be reconstructed from another with accuracy close to self-reconstruction benchmarks, especially in trivial phase regimes. This transferability relies on correct physical labeling, persists across finite-temperature regimes, and remains robust under noisy inputs. Our results suggest that separate observables can carry a substantial fraction of one another's physical information, providing numerical evidence for observable-level redundancy in the two-dimensional Fermi-Hubbard system.
