analiza aiqbench
This study applies neural-network reconstruction techniques to the Fermi-Hubbard model, demonstrating that distinct quantum observables carry substantial redundant physical information regarding phase diagrams. The findings highlight how machine learning frameworks can efficiently extract shared data from noisy, finite-temperature regimes in strongly correlated quantum systems.
Revealing Physical Redundancy in the Two-dimensional Fermi-Hubbard Model via Transferable Observable Reconstruction
arXiv:2608.20836v1 Announce Type: new Abstract: 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.
Abstract: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.
arXiv:2608.20836 [quant-ph]
(or arXiv:2608.20836v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2608.20836 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 235 cuvinte
Subiecte conexe

News
Quantum Computing Report · 7 iun. 2026
Does preservation make sense before we know how to revive?
LessWrong · 15 iun. 2026

Detection of a Rényi Index Dependent Transition in Entanglement Entropy Scaling
Quantum Journal · 7 aug. 2026

Non-Gaussian Noise Magnetometry Using Local Spin Qubits
Quantum Journal · 27 aug. 2026