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This research investigates the sensitivity of minimum gaps in sparse constraint-preserving quantum annealing, specifically analyzing how local driver modifications can trigger a "repair-and-expose" mechanism that reorganizes the low-energy subspace. The study demonstrates that while infinitesimal gap sensitivity reliably predicts beneficial changes, finite edge replacements require relinearizing responses to account for competing minima and bottleneck migration. These findings offer critical insights into optimizing annealing paths and managing ground state transitions in complex quantum systems.
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Gap Sensitivity and Bottleneck Migration in Sparse Constraint-Preserving Quantum Annealing

arXiv3 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2610.11262v1 Announce Type: new Abstract: Constraint-preserving exchange drivers allow many coupling graphs that preserve the same feasible sector, but exhibit different minimum gaps along an annealing path. We derive an exchange-resolved sensitivity of the normalised minimum gap for fixed-cardinality Laplacian drivers sharing a Dicke ground state, and apply it to identify locally favourable modifications of the driver. Numerical experiments indicate this sensitivity reliably predicts beneficial infinitesimal changes, but its reliability decreases when the same direction is extended to a finite edge replacement. The gap landscape tracking reveals one mechanism of this loss of reliability. Improving the active bottleneck can expose a competing minimum, with a more extensive reorganisation of the low-energy subspace. We call this the "repair-and-expose" mechanism and observe it in both dense quadratic and sparse frustrated objective families. This motivates relinearizing the response when competing minima become co-active. In the tested oracle setting, a tied-minimum max-min update is substantially more reliable than continuing the original direction or optimizing only the incoming minimum. Supporting finite-size search and selector tests show that this local information can remain useful beyond the infinitesimal limit, while its practical extraction depends on the driver geometry and identification of the relevant low-energy competitors. These results connect local exchange-resolved gap sensitivity to bottleneck migration and conditional driver redirection.

Abstract:Constraint-preserving exchange drivers allow many coupling graphs that preserve the same feasible sector, but exhibit different minimum gaps along an annealing path. We derive an exchange-resolved sensitivity of the normalised minimum gap for fixed-cardinality Laplacian drivers sharing a Dicke ground state, and apply it to identify locally favourable modifications of the driver. Numerical experiments indicate this sensitivity reliably predicts beneficial infinitesimal changes, but its reliability decreases when the same direction is extended to a finite edge replacement. The gap landscape tracking reveals one mechanism of this loss of reliability. Improving the active bottleneck can expose a competing minimum, with a more extensive reorganisation of the low-energy subspace. We call this the "repair-and-expose" mechanism and observe it in both dense quadratic and sparse frustrated objective families. This motivates relinearizing the response when competing minima become co-active. In the tested oracle setting, a tied-minimum max-min update is substantially more reliable than continuing the original direction or optimizing only the incoming minimum. Supporting finite-size search and selector tests show that this local information can remain useful beyond the infinitesimal limit, while its practical extraction depends on the driver geometry and identification of the relevant low-energy competitors. These results connect local exchange-resolved gap sensitivity to bottleneck migration and conditional driver redirection.

arXiv:2610.11262 [quant-ph]

(or arXiv:2610.11262v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2610.11262 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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