Tutorial T1

Title: QuantumFlow: A Co-Design Framework of Neural Network and Quantum Circuit towards Quantum Advantage

Description: This tutorial presents a recently proposed co-design framework for neural networks and quantum circuits. It comprises (1) a lecture session on quantum machine learning, including the fundamentals of quantum computing, an overview of quantum machine learning, and the mapping of neural networks onto quantum circuits; (2) a hands-on session using IBM Qiskit to program neuron computation with quantum circuits; (3) a lecture and hands-on hybrid session of co-design neural network and quantum circuit to optimize the quantum implementation with the consideration of noise in qubits. The tutorial targets experts and students in artificial intelligence, machine learning, quantum computing, compiler and architecture optimization, and hardware/software co-design and system synthesis.

Along with the rapid development of quantum computers, e.g., Google’s Sycamore with 53 qubits and IBM’s Hummingbird with 65 qubits, there is a growing interest in pursuing quantum supremacy or advantages over classical computers in various applications. Machine learning is one of the most promising applications because (1) it encounters the computation-/memory-bound on classical computing and (2) the linear algebra at its core is also central to quantum computing.

Organizers and speakers: 

Weiwen Jiang, George Mason University, USA

Jinjun Xiong, IBM T.J. Watson Research Centre, USA

Yiyu Shi, University of Notre Dame, USA

Biographies: Weiwen Jiang will join George Mason University as a Tenure-Track Assistant Professor. He is a Postdoctoral Associate at the University of Notre Dame. He received his Ph.D. degree from Chongqing University in 2019. From 2017 to 2019, he was a research scholar at the University of Pittsburgh. His research interest is on hardware and software co-design; in particular, the co-design of neural networks and different hardware accelerators, including mobile devices, FPGA, and ASIC. Most recently, he moves to the co-design of neural networks and quantum circuits. His work demonstrates the quantum advantages for neural networks for the first time, which has been published at Nature Communications. Dr. Jiang’s research works have been published in prestigious journals and conferences, including Nature Electronics, Nature Communications, IEEE/ACM Transactions, DAC, ICCAD, ESWEEK, GLSVLSI, etc. He is the receipt of Best Paper Award in ICCD’17 and Best Paper Nominations in DAC’19, CODES+ISSS’19, ASP-DAC’16, and ASP-DAC’20.

Jinjun Xiong is currently the Program Director for Cognitive Computing Systems Research at the IBM Thomas J. Watson Research Center. He is responsible for defining the scientific agenda and strategic directions for advanced cognitive computing systems research across industries, academia and governmental agencies. In that capacity, he co-directs the IBM-Illinois Center for Cognitive Computing Systems Research (C3SR). Prior to that role, Dr. Xiong was a manager of the Smarter Energy group, responsible for the IBM Research’s Big Bet Program on Smarter Energy Research, including its strategies and execution. Dr. Xiong received his Ph.D. degree in Electrical Engineering from University of California, Los Angeles in 2006, and since then has been a Research Staff Member with IBM Thomas J. Watson Research Center. His research interests include quantum computing, cognitive computing, big data analytics, deep learning, smarter energy and application of cognitive computing for industrial solutions.

Yiyu Shi is currently an associate professor in the Department of Computer Science and Engineering at the University of Notre Dame, the site director of NSF I/UCRC Alternative and Sustainable Intelligent Computing, and a visiting scientist at Boston Children’s Hospital, the primary pediatric program of Harvard Medical School. His current research interests focus on hardware intelligence with biomedical applications. He has published over 200 peer reviewed papers in premier venues such as Nature research journals, including more than a dozen best papers or nominations in top conferences. He was also the recipient of IBM Invention Achievement Award, Japan Society for the Promotion of Science (JSPS) Faculty Invitation Fellowship, Humboldt Research Fellowship, IEEE St. Louis Section Outstanding Educator Award, Academy of Science (St. Louis) Innovation Award, Missouri S&T Faculty Excellence Award, NSF CAREER Award, the Air Force Summer Faculty Fellowship, IEEE Computer Society Mid-Career Research Achievement Award, and Facebook Research Award.