Amortized generative recovery advantage across problem families

Establish whether a configuration-importance model trained once across a family of fermionic Hamiltonian instances can complete the physical-sector tail of a new under-sampled instance without per-instance retraining and outperform a strong classical selector.

Background

The paper evaluates a generative tail-completer and an amortized configuration-importance model for recovering spectral-support configurations from under-sampled quantum data. A single-instance generative model does not show a statistically resolved improvement over a cheap classical CIPSI baseline.

An amortized model trained across a six-instance Hubbard family generalizes to held-out instances and matches the classical baseline, but does not yet demonstrate a statistically resolved advantage. The authors therefore leave unresolved whether amortized learning can deliver a genuine improvement, particularly in high-noise and high-correlation regimes.

References

Amortization without per-instance retraining is therefore feasible---a train-once model generalizes across the family---but shows \emph{no statistically resolved advantage} over the classical baseline yet, the status of the open question: the single-instance negative result sets the bar, the amortized result meets but does not clear it, and---consistent with both---any advantage, if it exists, would be confined to the high-noise, high-correlation regime, never on the easy instances where the classical prior already wins.

— Dynamical spectral functions from bitstring-sampled quantum subspaces: entanglement, not one-body magic, tracks the sampling cost  (2608.16436 - Vargas, 17 Aug 2026) in Section 6, Discussion, subsection “Machine learning that preserves, rather than removes, the quantum sampling role”

The central next step is to identify favorable partitions from accessible chemical and structural information, and to determine whether their preparation and sampling benefits persist after these additional costs are included.

— Classical Active-Space Hybrid Quantum Subspace Expansion (CASH-QSE): Quantum Corrections without Remeasuring the Classically Calculable Energy  (2609.08170 - Izmaylov, 8 Sep 2026) in Section 8, Conclusions