Quantum Neural Networks (QNNs) represent a promising fusion of quantum computing and neural network architectures, offering speed-ups and efficient processing of high-dimensional, entangled data. A crucial component of QNNs is the encoder, which maps classical input data into quantum states. However, choosing suitable encoders remains a significant challenge, largely due to the lack of systematic guidance and the trial-and-error nature of current approaches. This process is further impeded by two key challenges: (1) the difficulty in evaluating encoded quantum states prior to training, and (2) the lack of intuitive methods for analyzing an encoder's ability to effectively distinguish data features. To address these issues, we introduce a novel visualization tool, XQAI-Eyes, which enables QNN developers to compare classical data features with their corresponding encoded quantum states and to examine the mixed quantum states across different classes. By bridging classical and quantum perspectives, XQAI-Eyes facilitates a deeper understanding of how encoders influence QNN performance. Evaluations across diverse datasets and encoder designs demonstrate XQAI-Eyes's potential to support the exploration of the relationship between encoder design and QNN effectiveness, offering a holistic and transparent approach to optimizing quantum encoders. Moreover, domain experts used XQAI-Eyes to derive two key practices for quantum encoder selection, grounded in the principles of pattern preservation and feature mapping.

翻译：量子神经网络（QNNs）代表了量子计算与神经网络架构的有前景融合，为高维纠缠数据提供了加速处理和高效计算能力。QNNs的关键组件是编码器，其负责将经典输入数据映射至量子态。然而，选择合适的编码器仍面临重大挑战，主要源于缺乏系统性指导以及当前方法依赖试错的性质。这一过程进一步受到两个关键挑战的阻碍：（1）在训练前评估编码量子态的困难性，以及（2）缺乏直观方法分析编码器有效区分数据特征的能力。为解决这些问题，我们提出了一种新型可视化工具XQAI-Eyes，使QNN开发者能够比较经典数据特征与其对应编码量子态，并检查不同类别间的混合量子态。通过桥接经典与量子视角，XQAI-Eyes促进了对编码器如何影响QNN性能的深入理解。跨多样化数据集和编码器设计的评估表明，XQAI-Eyes具备支持探索编码器设计与QNN效能关系的潜力，为优化量子编码器提供了全面且透明的方法。此外，领域专家利用XQAI-Eyes基于模式保持和特征映射原则，推导出量子编码器选择的两项关键实践准则。