Towards Explainable Quantum AI: Informing the Encoder Selection of Quantum Neural Networks via Visualization

• URL: http://arxiv.org/abs/2512.14181v1
• Date: Tue, 16 Dec 2025 08:21:47 GMT
• Title: Towards Explainable Quantum AI: Informing the Encoder Selection of Quantum Neural Networks via Visualization
• Authors: Shaolun Ruan, Feng Liang, Rohan Ramakrishna, Chao Ren, Rudai Yan, Qiang Guan, Jiannan Li, Yong Wang,
• Abstract summary: Quantum Neural Networks (QNNs) represent a promising fusion of quantum computing and neural network architectures.<n>A crucial component of QNNs is the encoder, which maps classical input data into quantum states.<n>XQAI-Eyes is a novel visualization tool that enables QNN developers to compare classical data features with their corresponding encoded quantum states.
• Score: 13.887583052955277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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