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VIOLET: Visual Analytics for Explainable Quantum Neural Network Speaker (s): 
RUAN Shaolun
PhD Candidate
School of Computing and Information Systems
Singapore Management University | Date:
Time:
Venue:
| | 4 April 2024, Thursday
9:30am - 10:00am
Meeting room 4.4, Level 4. School of Computing and Information Systems 1, Singapore Management University,
80 Stamford Road Singapore 178902
Please register by 3 April 2024.
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About the Talk 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. Two novel visualizations, 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.
This is a Pre-Conference talk for IEEE 17th Pacific Visualization Conference (PacificVis 2024). My paper was accepted by IEEE TVCG track for presentation.
About the Speaker Shaolun Ruan is currently a Ph.D. candidate in the School of Computing and Information Systems at Singapore Management University (SMU). His work focuses on developing novel graphics that enable a more effective and smoother analysis for humans using machines. His work focuses on handling complex and abstract domain problems like quantum computing and bioinformatics, leveraging the methods from Data Visualization and Augmented Reality. He is the first author of seven research papers, including five top-tier journal/conference papers including IEEE TVCG, IEEE VIS, CGF, etc. He received his bachelor's degree from the University of Electronic Science and Technology of China (UESTC) in 2019. For more information, kindly visit https://shaolun-ruan.com/. |
