Pre-conference talk by RUAN Shaolun and ZHANG Songheng

Talk #1: VENUS: A Geometrical Representation for Quantum State Visualization
by RUAN Shaolun, PhD Candidate

Visualizations have played a crucial role in helping quantum computing users explore quantum states in various quantum computing applications. Among them, Bloch Sphere is the widely-used visualization for showing quantum states, which leverages angles to represent quantum amplitudes. However, it cannot support the visualization of quantum entanglement and superposition, the two essential properties of quantum computing. To address this issue, we propose VENUS, a novel visualization for quantum state representation. By explicitly correlating 2D geometric shapes based on the math foundation of quantum computing characteristics, VENUS effectively represents quantum amplitudes of both the single qubit and two qubits for quantum entanglement. Also, we use multiple coordinated semicircles to naturally encode probability distribution, making the quantum superposition intuitive to analyze. We conducted two well-designed case studies and an in-depth expert interview to evaluate the usefulness and effectiveness of VENUS. The result shows that VENUS can effectively facilitate the exploration of quantum states for the single qubit and two qubits.

Talk #2: Don't Peek at My Chart: Privacy-preserving Visualization for Mobile Devices 
by ZHANG Songheng, PhD Candidate

Data visualizations have been widely used on mobile devices like smartphones for various tasks (e.g., visualizing personal health and financial data), making it convenient for people to view such data anytime and anywhere. However, others nearby can also easily peek at the visualizations, resulting in personal data disclosure. In this paper, we propose a perception-driven approach to transform mobile data visualizations into privacy-preserving ones. Specifically, based on human visual perception, we develop a masking scheme to adjust the spatial frequency and luminance contrast of colored visualizations. The resulting visualization retains its original information in close proximity but reduces the visibility when viewed from a certain distance or further away. We conducted two user studies to inform the design of our approach (N=16) and systematically evaluate its performance (N=18), respectively. The results demonstrate the effectiveness of our approach in terms of privacy preservation for mobile data visualizations.

Shaolun RUAN is currently a Ph.D. candidate in Computer Science at Singapore Management University under the supervision of Assistant Professor Yong WANG. Before that, he received his bachelor's degree from the University of Electronic Science and Technology of China, majoring in Information Security at the School of Computer Science and Engineering in 2019. From 2020 to 2021, he worked as a Research Assistant at Kent State University, U.S. His major research interests include Data Visualization, Human-Computer Interaction, and Quantum Computing.

Songheng Zhang is currently a Ph.D. candidate in the School of Computing and Information Systems at Singapore Management University.  His research interests include data visualization and visual analytics. For more details, please refer to https://alexanderzsh.github.io.