With the rapid development of Quantum Machine Learning, quantum neural networks (QNN) have experienced great advancement in the past few years, harnessing the advantages of quantum computing to significantly speed up classical machine learning tasks. Despite their increasing popularity, the quantum neural network is quite counter-intuitive and difficult to understand, due to their unique quantum-specific layers (e.g., data encoding and measurement) in their architecture. It prevents QNN users and researchers from effectively understanding its inner workings and exploring the model training status. To fill the research gap, we propose VIOLET, a novel visual analytics approach to improve the explainability of quantum neural networks. Guided by the design requirements distilled from the interviews with domain experts and the literature survey, we developed three visualization views: the Encoder View unveils the process of converting classical input data into quantum states, the Ansatz View reveals the temporal evolution of quantum states in the training process, and the Feature View displays the features a QNN has learned after the training process. Two novel visual designs, i.e., satellite chart and augmented heatmap, are proposed to visually explain the variational parameters and quantum circuit measurements respectively. We evaluate VIOLET through two case studies and in-depth interviews with 12 domain experts. The results demonstrate the effectiveness and usability of VIOLET in helping QNN users and developers intuitively understand and explore quantum neural networks

翻译：随着量子机器学习的快速发展，量子神经网络在过去几年中取得了重大进展，利用量子计算的优势显著加速了经典机器学习任务。尽管其日益流行，但由于架构中特有的量子专用层（如数据编码和测量），量子神经网络相当反直觉且难以理解。这阻碍了QNN用户和研究人员有效理解其内部工作机制并探索模型训练状态。为填补这一研究空白，我们提出VIOLET，一种新颖的可视分析方法来提升量子神经网络的可解释性。基于领域专家访谈和文献调研提炼的设计需求，我们开发了三个可视化视图：编码器视图揭示将经典输入数据转换为量子态的过程，拟设视图展示训练过程中量子态的时间演化，特征视图显示QNN在训练后学到的特征。提出两种新颖的视觉设计——卫星图和增强热力图——分别用于直观解释变分参数和量子电路测量。我们通过两个案例研究和12位领域专家的深度访谈评估了VIOLET。结果表明，VIOLET在帮助QNN用户和开发者直观理解和探索量子神经网络方面具有有效性和可用性。