aiqbench analysis
This research addresses critical error estimation in quantum simulations of lattice gauge theories by formalizing truncation uncertainties within the electric basis. By leveraging Hilbert space fragmentation, the study significantly refines accuracy bounds for models like the Schwinger model and pure U(1) theory, offering a substantial improvement over prior estimates. This advancement is vital for validating the feasibility of simulating complex quantum field theories on near-term quantum hardware.
Truncation uncertainties for accurate quantum simulations of lattice gauge theories
arXiv:2508.00061v4 Announce Type: replace Abstract: The encoding of lattice gauge theories onto quantum computers requires a discretization of the gauge field's Hilbert space on each link, which presents errors with respect to the Kogut--Susskind limit. In the electric basis, Hilbert space fragmentation has recently been shown to limit the excitation of large electric fields. Here, we leverage this to develop a formalism for estimating the size of truncation errors in the electric basis. Generically, the truncation error falls off as a factorial of the field truncation. Examples of this formalism are applied to the Schwinger model and a pure U(1) lattice gauge theory. For reasonable choices of parameters, we improve on previous error estimates by a factor of 10^{306}.
Abstract:The encoding of lattice gauge theories onto quantum computers requires a discretization of the gauge field's Hilbert space on each link, which presents errors with respect to the Kogut--Susskind limit. In the electric basis, Hilbert space fragmentation has recently been shown to limit the excitation of large electric fields. Here, we leverage this to develop a formalism for estimating the size of truncation errors in the electric basis. Generically, the truncation error falls off as a factorial of the field truncation. Examples of this formalism are applied to the Schwinger model and a pure U(1) lattice gauge theory. For reasonable choices of parameters, we improve on previous error estimates by a factor of 10^{306}.
arXiv:2508.00061 [quant-ph]
(or arXiv:2508.00061v4 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2508.00061 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 203 words
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