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Life sciences · Preprint
arXiv · August 19, 2026
Posted before peer review. The findings may change or fail to hold.
This preprint introduces Bernstein-Vazirani Networks, a non-variational quantum machine learning framework that uses quantum interference for supervised learning. Experiments on classification and image representation tasks report competitive performance relative to classical and quantum baselines, but the work has not undergone peer review and lacks detailed quantitative comparisons or effect sizes.
Preprint. Synthetic and real-world classification tasks, implicit image representation learning; no human subjects or clinical population. Intervention: Bernstein-Vazirani Networks (BVNs): a non-variational quantum machine learning framework leveraging quantum interference in standard Fourier basis or problem-adapted generalised bases for supervised learning. Compared with: Classical and quantum baselines (specific methods not named in abstract).
BVNs achieve universal function approximation through overcomplete interference bases Training of BVNs is gradient-free Framework demonstrates strong generalisation capabilities on synthetic and real-world classification tasks
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This is an unrefereed arXiv preprint presenting a novel quantum machine learning framework with experimental validation on synthetic and real-world tasks, but lacks peer review and independent confirmation.
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We introduce Bernstein-Vazirani Networks (BVNs), a non-variational quantum machine learning framework that leverages quantum interference for supervised learning, demonstrated on vision and representation learning tasks. In their standard form, BVNs follow the principle of quantum Fourier sampling: labelled data are placed in superposition and interfered in the Fourier basis to extract globally informative features. We then define generalised BVNs that enable interference in problem-adapted bases, yielding more expressive models under the same measurement budget as in the standard setting. BVNs achieve universal function approximation through (over)complete interference bases, while training of BVNs is gradient-free. Experiments on synthetic and real-world classification tasks, as well as implicit image representation, show strong generalisation capabilities and competitive performance with classical and quantum baselines.
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