Classical Intelligence For Quantum Technologies: An Evidence Roadmap For AI-Enabled Quantum-Computing Workflows

Amar Singh

doi:10.1002/qute.70495

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Classical Intelligence For Quantum Technologies: An Evidence Roadmap For AI-Enabled Quantum-Computing Workflows

2026-10-03

PRE

DOI：10.1002/qute.70495

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Abstract

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Artificial intelligence (AI) is becoming an operational part of quantum-computing workflows. Here, classical intelligence denotes classical learning, inference, and adaptive search—machine learning (ML), deep learning (DL), and bio-inspired optimization—that improves how a quantum workflow is controlled, executed, or interpreted. A structured narrative review maps these methods across the control, readout, compilation, and application layers. A reproducible search identified 379 records; 112 directly evaluative studies and 21 contextual records were retained. Each study was coded on four reporting dimensions as explicit, partial, or absent. Visibility was asymmetric (count, 95% Wilson interval): quantum budget 109/112 (92.4%–99.1%), classical overhead 72/112 (55.1%–72.6%), robustness 70/112 (53.3%–70.9%), and transfer 56/112 (40.9%–59.1%). A separate deployment-readiness judgment, derived from the evaluation regime, places 85/112 studies at simulator-only, 13/112 at mixed or cloud, and 14/112 at hardware validation; none met the controller-service criterion. A linked results layer records each study's comparator and direction of effect. ML is most defensible when uncertainty or reusable structure reduces quantum queries, DL when raw or high-dimensional structure dominates, and bio-inspired search when objectives are rugged or mixed discrete-continuous. The field's main limitation is incomplete end-to-end evidence on classical cost, drift, transfer, and deployment, not an absence of benefit.

Keywords:

bio-inspired optimization

classical intelligence

deep learning

evidence map

machine learning

quantum compilation

quantum control

quantum error correction

quantum technologies

variational quantum algorithms