Project Title: PPoSS:Planning:Software Stack for Scalable Heterogeneous NISQ Cluster
PIs: Vipin Chaudhary(CWRU), Qiang Guan(KENTSTATE), Samee Khan(MSSTATE), Xiaosong Li(UW)
Awards: 2216923 (CWRU, $143K), 2217021 (KENTSTATE, $44K), 2216896 (MSSTATE, $34K), 2216519 (UW, $21K),
Project Duration: 7/15/2022 - 6/30/2023
Development of large-scale and practical quantum computers is a priority for many countries, industries, and researchers. Demonstrating quantum computers at scale will change the computing model as it is currently known forever, enabling scientific discoveries at an unprecedented pace. This project’s novelties are in designing future quantum systems as a cluster of heterogeneous quantum computers. Such an approach is significantly different from all existing endeavors, as it will be cost effective, scalable, more usable, and more reliable. The project’s impacts include outlining the challenges in such systems, proposing solutions, engaging the community, and describing a plan to build a full software stack for such heterogeneous quantum-computing-based clusters. The project will also engage the multidisciplinary quantum computing community through three invited workshops to inform the potential path towards solutions for the challenges outlined. Through a backbone stakeholder committee, the project will ensure sustainable and sustained workforce development and broadening participation in computing objectives, outcomes, and impact at scale. In addition, the project personnel have a strong commitment to increasing participation of underrepresented groups (including women, racial minorities, and persons with disabilities) in planned activities.
This project explores the feasibility of designing a full software stack for a cluster of heterogeneous Noisy Intermediate-Scale Quantum (NISQ) machines. The project will make contributions to the: (a) Realization of cluster of heterogeneous NISQ machines as a quantum-computing platform with large-scale simulation and evaluation on a real platform; (b) Programming environment and user interface to provide a visual interface to understand quantum noise; (c) Compilation techniques to account for heterogeneity of NISQ machines and temporal errors; (d) Runtime to enable fault-tolerance, resource management and scheduling considering the queuing time and noise condition in real time with the help of a resource monitoring mechanism to query the calibration information from all available quantum computers; (e) Co-design of the stack with quantum machine learning and quantum chemistry applications; (f) Utilization of the system calibration data from the multiple existing quantum machines, then apply fidelity degradation detection on each noise attributes to generate the fidelity degradation matrix which is used to define multiple new evaluation metrics to compare the fidelity between the qubit topology of the quantum machines; and (g) Engagement of the multidisciplinary quantum computing community through three invited workshops to inform the potential path towards solutions for the challenges outlined. Education, workforce development (WFD) and broadening participation in computing (BPC) are a major priority of this project. These will be realized as: (a) Through a backbone stakeholder committee, the investigators will ensure sustainable and sustained WFD and BPC objectives, outcomes, and impact at scale. The project plan capitalizes on the breadth of expertise of the PIs with an overall strategy organized to reach increasingly larger stakeholder groups (starting from project members, the broader systems community, and finally to K-12 and non-affiliated professionals); (b) In addition, the project personnel have a strong commitment to increasing participation of underrepresented groups (including women, racial minorities, and persons with disabilities) in planned activities; (c) The investigators will incorporate research outcomes in multiple courses; and (d) The project will facilitate collaboration and synergy among systems researchers, and engage and partner with industry for technology transfer and commercialization.
Vipin Chaudhary (Case Western Reserve University): CWRU PI
Vipin Chaudhary A veteran of High-Performance Computing (HPC), Dr. Chaudhary has been actively participating in the science, business, government, and technology innovation frontiers of HPC for almost three decades. His contributions range from heading research laboratories and holding executive management positions, to starting new technology ventures. Most recently, he was a Program Director at the National Science Foundation where he was involved in many national initiatives and the Empire Innovation Professor of Computer Science and Engineering at SUNY Buffalo. He cofounded Scalable Informatics, a leading provider of pragmatic, high performance software-defined storage and compute solutions to a wide range of markets, from financial and scientific computing to research and big data analytics. From 2010 to 2013, Dr. Chaudhary was the Chief Executive Officer of Computational Research Laboratories (CRL), a wholly owned Tata Sons company, where he grew the company globally to be an HPC cloud and solutions leader before selling it to Tata Consulting Services. Prior to this, as Senior Director of Advanced Development at Cradle Technologies, Inc., he was responsible for advanced programming tools for multi-processor chips. He was also the Chief Architect at Corio Inc., which had a successful IPO in July, 2000. Dr. Chaudhary was awarded the prestigious President of India Gold Medal in 1986 for securing the first rank amongst graduating students at the Indian Institute of Technology (IIT). He received the B.Tech. (Hons.) degree in Computer Science and Engineering from the Indian Institute of Technology, Kharagpur, in 1986 and a Ph.D. degree from The University of Texas at Austin in 1992.
Samee Khan (Mississippi State University): MSSTATE PI
Samee Khan Samee U. Khan received a PhD in 2007 from the University of Texas. Currently, he is the James W. Bagley Chair Professor and Department Head of Electrical & Computer Engineering at the Mississippi State University (MSU). Before arriving at MSU, he was Cluster Lead (2016-2020) for Computer Systems Research at National Science Foundation and the Walter B. Booth Professor at North Dakota State University. His research interests include optimization, robustness, and security of computer systems. His work has appeared in over 400 publications. He is associate editor of IEEE Transactions on Cloud Computing and Journal of Parallel and Distributed Computing.
Qiang Guan (Kent State University): KENTSTATE PI
Qiang Guan is an assistant professor in Department of Computer Science at Kent State University, Kent, Ohio. Dr. Qiang Guan is the director of GUANS Lab . He is also a guest scientist of Los Alamos National Laboratory. He was a scientist in Data Science at Scale Team, at Los Alamos National Laboratory. He was mentored by Dr. Nathan DeBardeleben, Sean Blanchard and Dr. James Ahrens. He was the technical lead of BEE (Build and Execution Environment) project at Los Alamos National Laboratory. He obtained the Ph.D. degree in Computer Science and Engineering from University of North Texas, Denton, Texas, in 2014.
Xiaosong Li (Kent State University): UW PI
Xiaosong Li began his career with UW in 2005, where he now is the Harry and Catherine Jaynne Boand Endowed Professor of Chemistry and Associate Chair of Chemistry. He received his PhD in theoretical chemistry from Wayne State University. His research interests include computational and theoretical studies of chemical processes, time-dependent electronic structure theory, molecular dynamics, and methods for excited-state calculations.
Mark Novotny (Mississippi State University): MSSTATE CO-PI
Mark Novotny is a W. L. Giles Distinguished Professor at Mississippi State University, and an Elected Fellow of the American Association for the Advancement of Science and also American Physical Society for "original algorithm development and applications of computational statistical mechanics to equilibrium and non-equilibrium problems in condensed-matter physics and materials science.
Shuai Xu (Case Western Reserve University): CWRU CO-PI
Shuai Xu is an assistant professor at Case Western Reserve University, Department of Computer and Data Sciences, his research interest lies in theoretical computer science, with a focus on designing efficient algorithms and Fourier analysis of Boolean functions. He is also interested in quantum computing.
Ang Li (Pacific Northwest National Lab): Quantum Simulator
Bo Fang (Pacific Northwest National Lab): Quantum Simulator and Quantum Chemistry
Betis Baheri (Kent State University)
Priyabrata Senapati (Kent State University)
- [IOLTS '22] Betis Baheri, Qiang Guan, Vipin Chaudhary. "Quantum Noise in the Flow of Time: A Temporal Study of the Noise in Quantum Computers." The IEEE International Symposium on On-Line Testing and Robust System Design (IOLTS).
- [QCE '22] Qiang Guan, Betis Baheri, Zixhuan Xu, Ying Mao, Vipin Chaudhary, Shuai Xu, Bo Fang, "Pinpointing the System Reliability Degradation in NISQ Machines", IEEE Quantum Week (QCE), 2022
- [QCE '22] Daniel Chen, Betis Baheri, Vipin Chaudhary, Qiang Guan, Ning Xie, Shuai Xu, "Approximate Quantum Circuit Reconstruction", IEEE Quantum Week (QCE), 2022.
- [IEEE VIS '22] Shaolun Ruan, Yong Wang, Weiwen Jiang, Ying Mao, Qiang Guan, VACSEN: A Visualization Approach for Noise Awareness in Quantum Computing, IEEE VIS, 2022.
- VACSEN: A Visualization Approach for Noise Awareness in Quantum Computing (https://vacsen.github.io/)
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The Ohio State Center Quantum Information Science and Engineering (CQISE) Launching Event , Poster Presentation: "Quantum Error Characterization and Analysis on NISQ Machines".
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2022 George Mason University Quantum Computing Seminar Invited Talk for "Enabling robust quantum computer system by understanding errors from NISQ machines." Sep 2022.
This material is based upon work supported by the National Science Foundation under Grants No. 226923, 2217021, 2216519 and 2216896. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.