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

2025-12-16

AI Summary

Encoder selection in quantum neural networks (QNNs) lacks interpretable, systematic guidance, primarily because the quality and discriminative capacity of encoded quantum states cannot be reliably assessed prior to training. Method: We propose XQAI-Eyes, a novel visualization-based analytical tool that establishes the first interpretable bridge between classical data space and quantum state space. It integrates quantum state visualization, density matrix analysis, classical–quantum feature alignment mapping, and multi-class mixed-state heatmap rendering to enable intuitive comparison of classical data features with their encoded quantum distributions and quantitative assessment of inter-class separability. Contribution/Results: Leveraging XQAI-Eyes, we derive two general encoding design principles—“pattern preservation” and “feature mapping.” Extensive evaluation across multiple benchmark datasets demonstrates that XQAI-Eyes accurately predicts encoder performance, significantly enhancing QNN development efficiency and reproducibility.

Technology Category

• Machine Learning: Quantum Machine Learning
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Computer Vision: Visual Reasoning & Symbolic Representations

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Web Mining and Content Analysis: Web data visualization

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.

Problem

• Selecting suitable quantum encoders lacks systematic guidance and relies on trial-and-error
• Evaluating encoded quantum states before training is difficult and lacks intuitive analysis
• Understanding how encoders distinguish data features and influence QNN performance is challenging

Innovation

• Visualization tool XQAI-Eyes compares classical data and quantum states
• It enables analysis of encoder's feature distinction across different classes
• Supports exploration of encoder design impact on QNN performance