发表机构
QodeX Quantum（QodeX量子公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究监督二元分类中标准线性模型与单量子比特混合态模型的内在可解释性，通过比较发现单量子比特混合态模型是标准线性模型的“椭球体版本”，讨论了模型偏差影响，为无量子背景读者提供量子机器学习思路助力教学。
AI 中文摘要
我们刻画并比较了用于监督二元分类任务的标准线性模型与单量子比特混合态模型的内在可解释性。并行比较表明，用于二元分类的单量子比特混合态模型是标准线性模型分类的“椭球体版本”。具体而言，我们学习一个超椭球体而非超平面来对数据进行分类。我们讨论了两种模型的几何归纳偏差的影响，以及每个模型如何包含不同的特征重要性归纳偏差。这种简短的刻画为对量子零背景且仅熟悉机器学习中的线性分类的读者提供了一条通往量子机器学习思想的便捷途径。为支持机器学习教学，我们鼓励教师利用本文将量子机器学习思想平稳地引入本科机器学习课堂。
英文摘要
We characterize and compare the inherent interpretability offerings of a standard linear model with a single qubit mixed state model for the task of supervised binary classification. A side by side comparison reveals that a single qubit mixed state model for binary classification is just the ``ellipsoid version" of standard linear model classification. More precisely, rather than learning a hyperplane to classify data, we learn a hyperellipsoid. We discuss the consequences of the geometric inductive biases of both models, as well as how each model contains a different feature importance inductive bias. This short characterization offers an accessible route to quantum machine learning (ML) ideas for readers who have zero background in quantum and are only familiar with linear classification in ML. In support of ML pedagogy, we encourage instructors to utilize this piece to smoothly introduce quantum ML ideas into the undergraduate ML classroom.
