Tutorial T3

Title: Tutorial on QuantumFlow+VACSEN: A Visualization System for Quantum Neural Networks on Noisy Quantum Devic

Abstract: As one of the most popular machine learning algorithms, neural networks have been applied in a wide variety of applications, such as autonomous vehicles, simultaneous translation, and diagnostic medical imaging. With the increasing requirement on analyzing the large-scale data (e.g., 108 pixels for one 3D-CT medical image), neural networks encounter both memory-wall and compute-bound on classical computers. With the extremely high parallelism in representing and processing information, Quantum Computing is promising to address these limitations. But, how to make full use of the powerful quantum computers to accelerate neural networks is still unclear. QuantumFlow, published at Nature Communications last year, is an end-to-end framework to optimize neural networks onto a given quantum processor. Importantly, following the co-design philosophy, the developed quantum neurons in QuantumFlow demonstrate the quantum advantage. Meanwhile, VACSENis an online visualization system which provides the “easy to understand” visualization of the noise status on all available quantum computing nodes, recommends the most robust transpilation of circuit on the selected quantum computing node, and allows the real-time execution for a given quantum algorithm with noise awareness. In this tutorial, we will introduce how to conduct the co-design of neural networks and quantum circuits with QuantumFlow and VACSEN. We will have hands-on experience in implementing the neural network on the quantum circuit. Finally, targeting the near-term quantum computers, we will discuss how to leverage VACSEN to design quantum neural networks in the NISQ-Era. All attendees will leave with code examples that they can use as the backbone implementation to their own projects, and they will have access to VACSEN for the profiling of quantum devices.

Biographies of the Speakers

Weiwen Jiang: Dr. Jiang joined the ECE department at George Mason University as an Assistant Professor in Fall 2021. He was a Postdoctoral Associate at the University of Notre Dame. He received the Ph.D. degree from Chongqing University in 2019. From 2017 to 2019, he was a research scholar at the University of Pittsburgh. His research works have won Best Paper Awards in IEEE TCAD’21, ICCD’17, and NVMSA’15. He is the receipt of four Best Paper Nominations in ASP-DAC’16, DAC’19, CODES+ISSS’19, ASP-DAC’20, and the Top Winning Awards at IEEE Services Hackathon. He built the first co-design framework, QuantumFlow, to demonstrate the quantum advantage in designing neural network onto a quantum computer, which was published in Nature Communications. On the quantum topic, he was invited to give a contribution talk at IBM Quantum Summit 2020 and host tutorials at QuantumWeek-21, CODES+ISSS’21, ICCAD’21, and will host tutorials at DAC’22.

Qiang Guan: Dr. Guan is an assistant professor in Department of Computer Science at Kent State University, Kent, Ohio. Dr. Guan is the direct of Green Ubiquitous Autonomous Networking System lab (GUANS). He is also a member of Brain Health Research Institute (BHRI) at Kent State University. He was a computer scientist in Data Science at Scale team at Los Alamos National Laboratory before joining KSU. His current research interests include fault tolerance design for HPC applications; HPC-Cloud hybrid system; virtual reality; quantum computing systems and applications.

Yong Wang: is currently an assistant professor in School of Computing and Information Systems at Singapore Management University. He obtained his Ph.D. in Computer Science from Hong Kong University of Science and Technology in 2018. His research interests include information visualization, visual analytics and explainable machine learning. His research has won the Best Paper Award in IUI 2017, Best Paper Honorable Mention Awards in CHI 2022 and IEEE VIS 2021, and Best Poster Award in IEEE VIS 2019. For more details, please refer to http://yong-wang.org.