Bernstein-Vazirani Networks: Quantum Machine Learning by Interference

The paper introduces Bernstein‑Vazirani Networks (BVNs), a non‑variational quantum machine‑learning framework that uses quantum interference for supervised learning. BVNs operate by placing labelled data into superposition and performing interference in the Fourier basis to extract globally informative features, and they can be generalized to use problem‑adapted bases for greater expressiveness. The authors demonstrate that BVNs can achieve universal function approximation with gradient‑free training, and report strong generalisation and competitive performance on synthetic and real‑world classification tasks as well as implicit image representation.

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