Bernstein-Vazirani Networks: Quantum Machine Learning by Interference

2026-08-19

AI Summary

本文介绍了Bernstein-Vazirani网络，一种利用量子干涉进行监督学习的非变分量子机器学习框架，并在视觉和表示学习任务中进行了验证。

Abstract

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.

Problem

• quantum interference
• supervised learning
• Fourier basis
• feature extraction
• representation learning

Innovation

• Bernstein-Vazirani Networks
• Quantum Interference
• Supervised Learning
• Fourier Basis
• Gradient-Free