  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.
