伯恩斯坦-瓦齐里安网络：基于干涉的量子机器学习

中文摘要 AI 辅助

我们提出了伯恩斯坦-瓦齐里安网络（BVNs），这是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，已在视觉和表示学习任务中得到验证。标准形式下，BVNs遵循量子傅里叶采样原理：将带标签数据置于叠加态，并在傅里叶基中进行干涉以提取全局信息特征。随后我们定义了广义BVNs，其可在适配问题的基中进行干涉，在与标准设置相同的测量预算下生成更具表达力的模型。BVNs通过（过）完备干涉基实现通用函数逼近，且其训练无需梯度。在合成与真实世界分类任务及隐式图像表示上的实验显示，BVNs具备出色的泛化能力，且与经典及量子基线的表现相当。

英文摘要

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.

发表机构

• University of Siegen（锡根大学）
• Imperial College London（伦敦帝国学院）
• Halmstad University（哈尔姆斯塔德大学）
• Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）

机构由 AI 辅助整理，请以论文原文为准。