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

arXiv: https://arxiv.org/abs/2512.14181 · HTML: https://mlsanity.com/paper/2512.14181/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 55 references that are in the map.
- First posted on arXiv 16 Dec 2025.

## Builds on (most cited first, 25 of 25)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [RuleMatrix: Visualizing and Understanding Classifiers with Rules](https://mlsanity.com/paper/1807.06228/) (2018)
- [ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models](https://mlsanity.com/paper/1704.01942/) (2017)
- [Towards Better Analysis of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1604.07043/) (2016)
- [Quantum Data Encoding: A Comparative Analysis of Classical-to-Quantum Mapping Techniques and Their Impact on Machine Learning Accuracy](https://mlsanity.com/paper/2311.10375/) (2023)
- [M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis](https://mlsanity.com/paper/2107.08264/) (2021)
- [Quantum Multi-Agent Meta Reinforcement Learning](https://mlsanity.com/paper/2208.11510/) (2022)
- [Towards Quantum Federated Learning](https://mlsanity.com/paper/2306.09912/) (2023)
- [Visual Analysis of Discrimination in Machine Learning](https://mlsanity.com/paper/2007.15182/) (2020)
- [GNNLens: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks](https://mlsanity.com/paper/2011.11048/) (2020)
- [Variational Quanvolutional Neural Networks with enhanced image encoding](https://mlsanity.com/paper/2106.07327/) (2021)
- [Diagnosing Ensemble Few-Shot Classifiers](https://mlsanity.com/paper/2206.04372/) (2022)
- [VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models](https://mlsanity.com/paper/2108.02550/) (2021)
- [NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks](https://mlsanity.com/paper/2108.12931/) (2021)
- [DFSeer: A Visual Analytics Approach to Facilitate Model Selection for Demand Forecasting](https://mlsanity.com/paper/2005.03244/) (2020)
- [Toward Physically Realizable Quantum Neural Networks](https://mlsanity.com/paper/2203.12092/) (2022)
- [CNNComparator: Comparative Analytics of Convolutional Neural Networks](https://mlsanity.com/paper/1710.05285/) (2017)
- [QuantumEyes: Towards Better Interpretability of Quantum Circuits](https://mlsanity.com/paper/2311.07980/) (2023)
- [ATP: Adaptive Threshold Pruning for Efficient Data Encoding in Quantum Neural Networks](https://mlsanity.com/paper/2503.21815/) (2025)
- [VENUS: A Geometrical Representation for Quantum State Visualization](https://mlsanity.com/paper/2303.08366/) (2023)
- [NAS-Navigator: Visual Steering for Explainable One-Shot Deep Neural Network Synthesis](https://mlsanity.com/paper/2009.13008/) (2020)
- [Neural Networks Architecture Evaluation in a Quantum Computer](https://mlsanity.com/paper/1711.04759/) (2017)
- [Quantivine: A Visualization Approach for Large-scale Quantum Circuit Representation and Analysis](https://mlsanity.com/paper/2307.08969/) (2023)
- [Toward Human-Quantum Computer Interaction: Interface Techniques for Usable Quantum Computing](https://mlsanity.com/paper/2502.00202/) (2025)

## Abstract (arXiv)

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.