Quantum hybrid modules for AI.
The original QHM project explores quantum subroutines for AI. Its hardware records and attention questions lead into two focused follow-up studies.
Where the work goes next.
Two focused directions from the original project.
Hardware measurementsShot saturation and evidence limitsAttention samplingExact softmax from residual overlapsThe original public preprint remains available on Research Square. These follow-up manuscripts study separate questions and have their own journal submissions.
What the study shows.
A modular starting point.
QHM explores where quantum routines for overlap estimation, optimization, and search could sit inside a classical AI workflow.
Two questions became focused studies.
The hardware records led to a reanalysis of measurement limits. The attention work led to an exact residual-overlap sampling model.
Scope & details.
Quantum Hybrid Modules for AI: Attention, Optimization, and Verification on Near-Term Quantum Hardware
DOI 10.21203/rs.3.rs-9825475/v1CC BY 4.0
This original preprint is exploratory. The later hardware reanalysis and residual-sampling manuscript develop separate, focused questions; neither establishes an end-to-end quantum advantage.
Read the abstract
We develop Quantum Hybrid Modules (QHM) as an accounting framework for placing quantum subroutines inside classical AI pipelines while making data access, oracle construction, measurement, and readout costs explicit. The framework considers three conditional modules: thresholded query–key retrieval using coherent overlap estimation, QAOA for discrete subproblems, and Grover search for counterexamples to symbolic constraints. For sparse attention, the often-quoted O(√(n/k)) Grover cost finds one of k marked keys; returning all k keys requires Θ(√(nk)) overlap-oracle calls per query, plus Ω(k) output and aggregation work. We evaluate small instances using statevector and GPU simulation, cloud emulators, and Rigetti Cepheus and IonQ Forte hardware. The reproducible evidence is diagnostic rather than advantageous: exact quantum kernels and hybrid variational layers do not beat tuned classical surrogates on the tested tasks; selected QAOA simulations produce good expected cuts but do not outperform the Goemans–Williamson algorithm; idealized Grover experiments recover the expected oracle-query scaling only when a reversible predicate oracle is assumed; and shallow hardware overlap circuits retain biased signal while routed superconducting QAOA cost observables approach their random baselines. A source-and-artifact audit excludes cross-provider QAOA ratio comparisons and several exploratory attention, verification-loop, retrieval, and post-hoc mitigation analyses whose stored implementations use inconsistent metrics or do not implement the claimed primitive. No end-to-end or near-term quantum advantage is demonstrated. The main contribution is a set of corrected resource conditions and empirical failure modes that future quantum-AI module claims must satisfy.
Citation
@article{lex2026qhm,
title={Quantum Hybrid Modules for {AI}: Attention, Optimization, and
Verification on Near-Term Quantum Hardware},
author={Lex, Vikram},
year={2026},
month={6},
journal={Research Square},
publisher={Research Square},
doi={10.21203/rs.3.rs-9825475/v1},
note={Public preprint, version 1},
url={https://doi.org/10.21203/rs.3.rs-9825475/v1}
}