Summary
Ensuring trust in combined quantum and classical computing processes can be tricky because some issues go undetected not due to lack of data but because of structural limits in how observations are made. This paper presents a framework to understand and test data integrity in such hybrid workflows, showing where traditional methods might miss problems and how specialized approaches can detect certain changes. The authors tested their methods on thousands of cases and used advanced statistical tools to measure how well the integrity checks work. The work helps clarify when and how workflows mixing quantum and classical steps can be trusted or might be vulnerable to hidden errors.
What this means in practice
- •For quantum computing engineers: Develop integrity tests to detect subtle errors and blind spots when combining quantum and classical computations in hybrid workflows.
- •For machine learning practitioners: Use geometry-aligned statistical tests to verify robustness of labels and predictions when mixing quantum simulation data with classical models.
Authors
Roberto Fernández-Barrios, Iker Pastor-López, Amaia Pikatza-Huerga, Pablo García Bringas
Abstract
We present a claim-relative evidence/reference framework for hybrid quantum-classical workflow integrity. Observational indistinguishability yields structural blind regions, distinct from finite-batch statistical misses. Within the declared lattice, a trusted same-batch scalar $R_0$ suffices for conclusion integrity, aggregate $M_0$ for aggregate plus conclusion integrity, and item-aligned binding for item identity. In 3,600 label interventions, feature/prediction views realize exact label-path invariance; all 764 geometry-aligned aggregate-blind rows equal their paired-clean responses, giving zero attack-only increment. For statistical response, the geometry-aligned construction detects 343/2,700 conclusion-changing ($τ\to 0^+$) label interventions with the conformal rule and 1,183/2,700 with the uncorrected union; the original frozen same-item geometry yields 11/2,617 and 43/2,617, respectively. The executed conformal clean false-action rates are 0.048--0.059 descriptively; its finite-sample guarantee requires exchangeability, which the overlapping-draw design violates. The cluster-preserving adaptive stress test (Gate A) reduces response versus matched controls in 25--40 of 40 environment/split cells while retaining conclusion changes. A bounded 165-design-cell ideal-statevector and finite-shot-emulation branch directly instantiates semantic, estimated and observed kernel transitions. The fixed equal-weight design estimates neither deployment prevalence nor QPU, provider or deployed-service assurance.