  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
