Year: 2026
Type: preprint
Abstract: The field of quantum machine learning (QML) evolved to value models believed to most directly rival those providing utility in classical ML, namely large-scale neural networks. This pursuit of quantum neural-network analogues has produced important theoretical insights for expressivity and trainability at scale; however, due to our inability to conduct large-scale empirical studies, this direction has contributed to skepticism that QML will provide a compelling application for quantum computers. In the meantime, classical ML has been learning a hard lesson with respect to deploying un-interpretable neural networks in the wild: model interpretability matters for human adoption. We adopt this broader ML perspective to encourage a research direction in QML that prioritizes discovering and understanding tools from quantum information theory that applied ML researchers can use to construct inherently interpretable models: models with intentional mathematical structure that guarantees domain-aligned behavior. We first exemplify this research direction through a thread of work in a neighboring area---Bayesian ML. Specifically, we discuss an efficient mathematical tool that emerged from a connection between Gaussian processes and Bayesian linear models, which has since been widely studied and used by applied researchers for problem-specific model design. We then discuss how prior QML research can be reframed as progress towards this direction, despite having been pursued with different goals in mind. Finally, we propose an initial sketch of future work in this direction, with the broader aim of better supporting the needs of applied ML researchers.
Source: arXiv (Cornell University)
Authors: Gili, Kaitlin, Bradshaw, Zachary P.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7169169146