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Nicolás Bonilla Vargas

Publications and source records attributed to Nicolás Bonilla Vargas.

2 recordsLinked to original sources

Captured weight and boundary leakage bound the error of sample-based spectral functions

Rayleigh-Ritz on a subspace from sample-based quantum diagonalization yields the spectral function of a probe state; we ask what bounds its error. The captured Born weight w does not do so alone: an analytic counterexample forces any bound indexed on it to its trivial value. On Hubbard rings at broadening $η=0.18t$, the probe's whole support (w=1) still has relative error 0.35--0.43: Rayleigh-Ritz displaces the poles a subspace keeps. The error is bounded instead by the missed weight and the Hamiltonian coupling across the subspace boundary. Given the exact ground state, we prove for any orthogonal projector that the relative $L_1$ error lies between 1-w and $\min\{1+w,(1+\sqrt{w})(\sqrt{1-w}+Λ(η)/η)\}$, with $Λ$, that coupling at resolution $η$, computed from the Ritz pairs and the retained probe. We test it on L-site rings with classical Born draws of an exactly time-evolved probe, or their infinite-shot ranking; our device runs are execution records only. On the ranked subspaces the bound is vacuous at every operating fraction of the resource scan, at all five sizes up to a 10306296-determinant sector: its leakage branch exceeds the trivial bound 1+w by factors 1.66--8.61. It is informative only well above those fractions, where it is calibrated: at L=6--12 it first beats the trivial bound at a true error of (1.2--7.3)$\times10^{-3}$, though an unproven fit in 1-w is tighter on 11 of 14 splits. For the Born-ranked $A(k,ω)$ at 85% of its sector, the bound is 350--900 times the true error. In the determinant basis, zero leakage at full weight needs every symmetry-allowed determinant: the probe's measured Krylov support at L=6--14. Plane-wave orbitals reduce a momentum probe's support to one block, 1/L of an exponentially growing sector. At full weight a second-order certificate follows; below it, where every operating fraction lies, one is open.

quant-ph↗

Machine learning for sample-based quantum diagonalization: a review of generative configuration recovery and the classical-simulability frontier

Sample-based quantum diagonalization (SQD), equivalently quantum-selected configuration interaction (QSCI), has become a centre of gravity of pre-fault-tolerant quantum chemistry: a processor samples electronic configurations and the Hamiltonian is diagonalized classically in the resulting subspace. Accuracy is governed entirely by which configurations enter it -- a machine-learning selection problem, made acute by a coupon-collector bottleneck. We review the generative and learned selectors by what each generates and the signal it exploits, and identify one gap: no reward-proportional generative-flow-network proposer has been built for tail discovery. On the field's central question -- whether the quantum sampler beats classical selected CI -- the negative verdict is not ours to claim: priority belongs to Reinholdt et al. [JCTC 21, 6811 (2025)], and polynomial-time classical estimation of the flagship circuits has reinforced it. We state that verdict at the precision a falsifiable claim requires -- it concerns reproducible, same-active-space comparisons on molecular electronic structure -- and weigh the claims outside those qualifiers. We show that alpha-string weights are not invariant under rotations inside degenerate orbital shells, so determinant counts are undefined until the orbital gauge is declared. We distil a ten-element benchmarking standard and apply it to our own deposit, which returned defects that changed numbers printed here and retracted one from v1. FCI-exact experiments confirm one prediction and refute another: the single generative advantage we find keeps no consistent sign along the dissociation coordinate at device-calibrated noise and reverses under a symmetric readout model. It does beat a noise-matched classical recovery loop on N2 by a margin five seeds cannot resolve, and loses by over a factor of two to a classical selector that needs no sampler.

quant-ph↗