With a 32-year career as a computer scientist and now affiliate researcher at the Lawrence Berkeley National Laboratory, E. Wes Bethel joined the San Francisco State University faculty in Fall 2022. As a computer science associate professor at the university, his research focuses on high-performance computing, computer organization and architecture, quantum computing, and applied artificial intelligence/machine learning. His recent paper, “Quantum Computing and Visualization: A Disruptive Technological Change Ahead,” details how visualization and quantum computing work together.
Learn more about his work and download his published article below.
What is the overall scope of your published paper, “Quantum Computing and Visualization: A Disruptive Technological Change Ahead,” and what inspired the research?
The piece introduces quantum computing concepts, such as quantum state of single- and multi-qubit systems, then provides examples of methods for visualizing quantum state. The piece also provides a brief survey of work in the nexus of quantum computing and visualization.
Our aim is to increase awareness within the visualization and graphics community of quantum computing and its potential. Some motivation stems from the technological evolution of classical computing and the trajectories of Dennard Scaling and Moore’s Law: there are limits to transistor density and clock rate that in turn will constrain what we can do with classical computing approaches. In contrast, quantum computing offers the promise of vast scaling far beyond anything possible in classical computing yet there are significant technical challenges to get there, both in terms of the quantum platforms themselves but also the way we think about and design algorithms. From a graphics and visualization perspective, we have a lot to learn about how to take advantage of this new type of computing paradigm, and this article intends to shed light on the foundations of quantum computing and potential opportunities. Our article just scratches the surface of these challenges, but we have to start somewhere (smiley face).
What made you decide that CG&A was the right platform to publish your work? Have you contributed to the publication before?
We felt that the Visualization Viewpoints column in CG&A would be a good venue for reaching a broad graphics and visualization audience. I personally have worked with Theresa-Marie Rhyne, CG&A Visualization Viewpoints Department Editor, in various capacities in the field of scientific visualization over the course of multiple decades. Her Visualization Viewpoints column is a good forum for presenting material that, while well grounded in scientific fact and method, may still be more forward-looking than might be appropriate for other types of venues. Her column does a great job of welcoming new ideas and viewpoints, which in turn helps the field to grow and evolve.
Tell us more about your background, such as your time as an Associate Professor of Computer Science at San Francisco State University, the focus area of your research, and why this field is important.
I joined the SFSU CS faculty in Fall 2022 after a 32-year career as a computer scientist at Lawrence Berkeley National Laboratory. At SFSU, my pedagogical and research focus spans multiple areas: high-performance computing, computer organization and architecture, quantum computing, and applied artificial intelligence/machine learning.
For the topic of AI, we all see speculation every day in the news that AI will replace coders, artists, authors, and so on. It is my view, and that of many of my colleagues, that recent advances in AI technologies create exciting and challenging new opportunities for both learning and advancing the state of the practice in terms of how we do research, write code, and more. The SFSU CS program has a graduate certificate program in Ethical AI, which is not my area, but it is an important part of the landscape. My personal interest is in helping students learn how to make effective use of these tools in a way that enhances their learning, rather than creating learning shortcuts.
I’m also very interested in topics related to HPC and quantum computing. Having worked in the classical HPC space for multiple decades, the technology trends are quite clear: we long ago reached the limits of Dennard Scaling and are now bumping up against Moore’s Law limits since we simply can’t make wires and transistors any smaller. What’s next? Data storage will likely continue in the classical regime for some time to come, but new methods for doing computations that leverage the seemingly limitless scalability of quantum platforms is a very interesting problem area.
Can you share more about your experience at Lawrence Berkeley National Laboratory, and how that translates into your work today?
While at LBNL, I was a senior computer scientist in the Computational Research Division. The broad mission was to advance computing technology in support of the Energy Department’s science mission. My area of focus was at the intersection of high-performance computing, scientific visualization, computer graphics, machine learning, computer organization/architecture, and to some extent, data management. Among the accomplishments there I’m particularly proud of is making production quality, petascale capable scientific visualization a reality at the Energy Department’s Computational Science User Facilities, the supercomputer centers. Over the years, I mentored and cultivated the careers of dozens of PhD-level computer scientists. One of the amazing things about working at a national laboratory is the highly multidisciplinary nature of team projects: it is commonplace for teams to consist of one or more domain scientists, e.g., plasma physics, along with a mixture of computer scientists and applied mathematicians. This kind of multidisciplinary partnering is part of the lab culture and it produces some very amazing scientific results.
In the present at SFSU, my mission is to help train the next generation of scientists and engineers. My background doing work in scientific computing at LBNL allows me to bring a somewhat unique perspective to the program, namely building multidisciplinary teams, and providing new opportunities for SFSU students, many of whom come from underrepresented groups, with exposure to HPC curricula with its roots in the Energy Department’s HPC programs as well as access to HPC facilities. My students have the opportunity to conduct HPC code development, performance analysis, and optimization on world-class supercomputing facilities operated by the Energy Department. In addition, the ongoing partnership in quantum computing with colleagues from LBNL represents another amazingly unique opportunity for the SFSU community.
Access the paper, “Quantum Computing and Visualization: A Disruptive Technological Change Ahead”
The article explores how visualization can help in understanding Quantum Computing (QC), and examines how it might be useful for visualization with the growth and maturation of both technologies in the future. In a quickly evolving technology landscape, QC is emerging as a promising pathway to overcome the growth limits in classical computing.
