量子机器学习通过全局相关性实现少样本的条形码相似性分类
Can Geometric Quantum Machine Learning Lead to Advantage in Barcode Classification?
- 基于对称性自适应测量的几何量子模型
- 量子网络显著优于经典神经网络
- 适合小样本、高复杂度数据分类任务
我们研究区分两组向量（可视化为图像或条形码）并判断其是否相关的问题。为此，提出一种嵌入对称性的几何量子机器学习（GQML）方法，可根据全局相关性对相似与不相似对进行分类，并实现仅用少量样本即可泛化。不同于以往的GQML算法，本工作聚焦于对称性感知的测量自适应策略，其性能优于酉参数化方法。在相似性测试中，对比了经典深度神经网络与卷积神经网络（孪生架构）。结果表明，量子网络整体显著优于经典模型。性能差异源于数据集构建所用的相关分布特性。该问题与已知存在BQP与多项式层级间最大分离的计算难题相关。尽管优势依赖于数据加载方式，但类似问题可从量子机器学习中获益。
原文摘要 · Abstract (English)
We consider the problem of distinguishing two vectors (visualized as images or barcodes) and learning if they are related to one another. For this, we develop a geometric quantum machine learning (GQML) approach with embedded symmetries that allows for the classification of similar and dissimilar pairs based on global correlations, and enables generalization from just a few samples. Unlike GQML algorithms developed to date, we propose to focus on symmetry-aware measurement adaptation that outperforms unitary parametrizations. We compare GQML for similarity testing against classical deep neural networks and convolutional neural networks with Siamese architectures. We show that quantum networks largely outperform their classical counterparts. We explain this difference in performance by analyzing correlated distributions used for composing our dataset. We relate the similarity testing with problems that showcase a proven maximal separation between the BQP complexity class and the polynomial hierarchy. While the ability to achieve advantage largely depends on how data are loaded, we discuss how similar problems can benefit from quantum machine learning.
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