Although quantum machine learning has been introduced for a while, its
applications in computer vision are still limited. This paper, therefore,
revisits the quantum visual encoding strategies, the initial step in quantum
machine learning. Investigating the root cause, we uncover that the existing
quantum encoding design fails to ensure information preservation of the visual
features after the encoding process, thus complicating the learning process of
the quantum machine learning models. In particular, the problem, termed
"Quantum Information Gap" (QIG), leads to a gap of information between
classical and corresponding quantum features. We provide theoretical proof and
practical demonstrations of that found and underscore the significance of QIG,
as it directly impacts the performance of quantum machine learning algorithms.
To tackle this challenge, we introduce a simple but efficient new loss function
named Quantum Information Preserving (QIP) to minimize this gap, resulting in
enhanced performance of quantum machine learning algorithms. Extensive
experiments validate the effectiveness of our approach, showcasing superior
performance compared to current methodologies and consistently achieving
state-of-the-art results in quantum modeling.

通过研究量子机器学习在计算机视觉中的应用，本文发现现有的量子编码设计未能保证在编码过程后信息特征的保留，给量子机器学习模型的学习过程带来了复杂性，同时也导致了一个被称为 “量子信息差距” 的问题，该问题直接影响了量子机器学习算法的性能。为了解决这个挑战，我们引入了一种名为量子信息保持的简单而高效的新损失函数（QIP），以最小化这个差距，从而提升了量子机器学习算法的性能。广泛实验证明了我们的方法的有效性，相比当前方法，提供了卓越的性能，并始终在量子建模中取得了最先进的结果。