Quantum Computing System Lecture Series
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1. Introduction Session Speaker: Yongshan Ding
Assistant Professor at Yale University
Title: Software and Algorithmic Approaches to Quantum Noise Mitigation: An Overview
Abstract: I will talk about recent developments in noise mitigation techniques for quantum computers. In the Noisy Intermediate-Scale Quantum (NISQ) era, qubits have short lifetimes and quantum gates are prone to errors. Understanding and mitigating quantum noises becomes a crucial step in realizing practical quantum computation. In this talk I will first give an overview of the field and its related research questions. We will then cover the techniques and recent results in systems software and algorithm designs for noise mitigation.
Bio: Yongshan Ding is an Assistant Professor of Computer Science at Yale University. He is a member of the Yale Quantum Institute (YQI) and is affiliated with the Computer Systems Lab (CSL). Ding completed his Ph.D. from the University of Chicago. He is a recipient of the William Rainey Harper Dissertation Fellowship, one of UChicago’s highest honors, and the Siebel Scholarship. Prior to that, he received his B.Sc. degrees in Computer Science and Physics from Carnegie Mellon University. Ding is also the lead author of a textbook, Quantum Computer Systems, in Morgan-Claypool Publisher’s Synthesis Lectures in Computer Architecture.
Time: July 07, Thursday, 10:30 ET
2. Introduction Session Speaker: Zhixin Song
PhD Student at Georgia Institute of Technology
Title: A Guided Tour on the Map of Quantum Computing
Abstract: In this talk, Zhixin will walk through basic concepts of Quantum Computing (QC), including qubit, quantum gates, and measurement. No background knowledge is required from the audience. Then, he will introduce different types of quantum hardware and how to benchmark their performance. Moreover, application-based near-term quantum algorithms and intermediate level (between software and hardware) research will also be addressed briefly. Finally, Zhixin will provide some suggestions and resources for self-learning.
Bio: Zhixin Song is currently a Physics Ph.D. student at Georgia Institute of Technology. He has previously done an internship at Baidu Research, Institute for Quantum Computing, working on Variational Quantum Algorithms (VQAs) and developing a Quantum Machine Learning toolkit called Paddle Quantum. His current research focuses on experimental quantum simulation, control theory, and quantum many-body systems.
Time: July 14, Thursday, 10:30 ET
3. Introduction Session Speaker: Jinglei Cheng
PhD Student at University of Sourthen California
Title: Introduction to Variational Quantum Algorithms
Abstract: Variational quantum algorithms are gaining more attention due to its resilience to noises. In this talk, Cheng will briefly introduce the principles of variational quantum algorithms. And he will demonstrate the performance of variational algorithms under a variety of types and strengths of noise. Examples of variational quantum algorithms will be given and discussed.
Bio: Jinglei Cheng is an EE PhD student at University of Southern California, supervised by Prof. Xuehai Qian at the ALCHEM research group. Prior to joining USC, he obtains the B.S. degree from Tsinghua University. He has been working on optimizing and accelerating variational quantum algorithms.
Time: July 21, Thursday, 10:30 ET
4. Introduction Session Speaker: Siyuan Niu
PhD Candidate at University of Monteplier
Title: Enabling Parallel Circuit Execution on NISQ Hardware
Abstract: Today’s quantum computers are in the Noisy Intermediate-Scale Quantum era and prone to errors. Only small circuits with shallow depth can be executed on NISQ hardware to get reliable results, which leads to the quantum hardware under-utilization issue. Parallel circuit execution can help improve the hardware resource utilization and reduce the total runtime of the circuits. However, the parallel operations can introduce crosstalk error. In this talk, I first explain crosstalk and how to characterize it on the quantum computer. Then, I discuss the design challenges and methods to enable parallel circuit execution. Finally, I provide several applications of parallel circuit execution on advancing NISQ computing.
Bio: Siyuan Niu received the B.S. degree in electronic and information engineering from Xidian University, China in 2018, M.S. degree in electronic engineering from Polytech Nice Sophia, France, in 2019. She is currently a third-year Ph.D candidate in quantum computing from the University of Montpellier. Her research interests focus on quantum compiler and quantum error mitigation.
Time: July 28, Thursday, 10:30 ET
4. Introduction Session Speaker: Robert Wille
Distinguished Professor at Technische Universität München
Title: Design Automation and Software Tools for Quantum Computing
Abstract: Quantum computing is becoming a reality, but automated methods and software tools for this technology are just beginning. The talk aims to provide an overview of automated and efficient methods for developing quantum computing, e.g., for simulating, compiling, or verifying quantum circuits. Design automation quantum computing will take advantage of design automation techniques that have proven to be powerful for traditional circuits and systems, but are difficult to exploit in quantum computing.
Bio: Robert Wille is a Full and Distinguished Professor at the Technical University of Munich, Germany, and Chief Scientific Officer at the Software Competence Center Hagenberg GmbH, Austria. He served as lecturer at the University of Applied Science Bremen from 2010 to 2012 and was guest professor at the University of Potsdam in 2012 as well as the Technical University of Dresden in 2013/2014. From 2015 to 2022, he was Full Professor at the Johannes Kepler University Linz and head of the Institute for Integrated Circuits (at the age of 32 and as one of the youngest full professors in the field). In 2019, he founded the LIT Secure and Correct Systems Lab at JKU and, in 2020, he additionally became Chief Scientific Officer at the Software Competence Center Hagenberg GmbH. Since 2022, he works in Munich—appointed through a “Leuchtturm”-procedure (i.e., direct appointment) and, additionally, through the “Distinguished Professorship”-program.
Time: Aug. 04th, Thursday, 10:30 ET
6. Introduction Session Speaker: Tongyang Li
Assistant Professor at Peking University
Title:Adaptive Online Learning of Quantum States
Abstract:Shadow tomography is a fundamental problem in quantum computing, whose goal is to efficiently learn an unknown d-dimensional quantum state using projective measurements. However, it is rarely the case that the underlying state remains stationary: changes may occur due to measurements, environmental noise, or an underlying Hamiltonian state evolution. In this paper we adopt tools from adaptive online learning to learn a changing state, giving adaptive and dynamic regret bounds for online shadow tomography that are polynomial in the number of qubits and sublinear in the number of measurements. In addition, our numerical experiments also corroborate our theories. In the future, it is of general interest to find applications of adaptive online learning to quantum computing in the NISQ era, including quantum machine learning, noise mitigation, etc. The paper is available on arXiv: https://arxiv.org/abs/2206.00220
Bio:Dr. Tongyang Li is currently an assistant professor at Center on Frontiers of Computing Studies, Peking University. Previously he was a postdoctoral associate at the Center for Theoretical Physics, Massachusetts Institute of Technology, during 2020-2021. He received Ph.D. degree from the Department of Computer Science, University of Maryland in 2020. He received Bachelor of Engineering from Institute for Interdisciplinary Information Sciences, Tsinghua University and Bachelor of Science from Department of Mathematical Sciences, Tsinghua University, both in 2015. Dr. Tongyang Li’s research focuses on designing quantum algorithms for machine learning and optimization. In general, he is interested in better understanding about the power of quantum algorithms, including topics such as quantum query complexity, quantum simulation, and quantum walks. He was a recipient of the IBM Ph.D. Fellowship, the NSF QISE-NET Triplet Award, and the Lanczos Fellowship.
Time: Aug 11th, Thursday, 10:30 ET
7. Introduction Session Speaker: Junyu Liu
Theoretical Physicist at the University of Chicago
Title: Quantum Data Center
Abstract: We propose the Quantum Data Center (QDC), an architecture combining Quantum Random Access Memory (QRAM) and quantum networks. We give a precise definition of QDC, and discuss its possible realizations and extensions. We discuss applications of QDC in quantum computation, quantum communication, and quantum sensing, with a primary focus on QDC for T-gate resources, QDC for multi-party private quantum communication, and QDC for distributed sensing through data compression. We show that QDC will provide efficient, private, and fast services as a future version of data centers.
Bio: Dr. Junyu Liu(刘峻宇) is a theoretical physicist currently working for the University of Chicago and IBM, associated with the Chicago Quantum Exchange with a maximally five-year position. He is primarily located in Uchicago in Prof. Liang Jiang’s Group in the Pritzker School of Molecular Engineering, and also a Kadanoff fellow in Kadanoff Center for Theoretical Physics. He is interested in theoretical physics and its relation to computation, including machine learning, optimization, quantum computing, data science, data security and the commercial value of modern computing technologies. He graduated from California Institute of Technology with a PhD in physics in June 2021, with the working experiences from the Walter Burke Institute for Theoretical Physics and the Institute for Quantum Information and Matter, supervised by Clifford Cheung, John Preskill and David Simmons-Duffin.
Time: Aug 18th, Thursday, 10:30 ET
8. Introduction Session Speaker: Gokul Ravi
Computing Innovation Fellow at the University of Chicago
Title: Classical Support and Error Mitigation for Variational Quantum Algorithms
Abstract: In this seminar Dr. Ravi will talk about Variational Quantum Algorithms (VQAs) and two proposals to improve their quality of execution on NISQ devices. First, Dr.Ravi will discuss VAQEM, which dynamically tailors existing error mitigation techniques to the actual, dynamic noisy execution characteristics of VQAs on a target quantum machine. This is done by tuning specific features of these mitigation techniques similar to VQAs’ traditional rotation angle parameters. In this work, we target two types of error mitigation techniques which are suited to idle times in quantum circuits: single qubit gate scheduling and the insertion of dynamical decoupling sequences. Second, Dr.Ravi will discuss finding a good ansatz initialization for VQAs through CAFQA, a Clifford Ansatz For Quantum Accuracy. The CAFQA ansatz is a hardware-efficient circuit built with only Clifford gates. In this ansatz, the parameters for the tunable gates are chosen by searching efficiently through the Clifford parameter space via classical simulation. The resulting initial states always equal or outperform traditional classical initialization and enable high-accuracy VQA estimations. CAFQA is well-suited to classical computation because: a) Clifford-only quantum circuits can be exactly simulated classically in polynomial time, and b) the discrete Clifford space is searched efficiently via Bayesian Optimization.
Bio: Dr. Gokul Ravi is a 2020 NSF Computing Innovation Fellow postdoc at the University of Chicago. His research targets classically inspired innovations for quantum computing and is mentored by Prof. Fred Chong. He received his PhD in Computer Architecture from UW-Madison in 2020 and was advised by Prof. Mikko Lipasti. During his PhD he was awarded the 2020 Best ECE Dissertation Award and recognized as a 2019 Rising Star in Computer Architecture.
Time: Aug 25th, Thursday, 10:30 ET
9. Introduction Session Speaker: Chen Qian
Professor at UCSC
Title: Protocol Design for Quantum Network Routing
Abstract: Quantum entanglement enables important computing applications such as quantum key distribution. Based on quantum entanglement, quantum networks are built to provide long-distance secret sharing between two remote communication parties. Establishing a multihop quantum entanglement exhibits a high failure rate. When the scale of a quantum network increases, it requires end-to-end multi-hop quantum entanglements in order to deliver quantum bits without letting the repeaters know the bits. This talk presents a very early effort to design a scalable protocol for entanglement routing. We present a comprehensive entanglement routing model that reflects the differences between quantum networks and classical networks as well as a new entanglement routing algorithm that utilizes the unique properties of quantum networks. We hope our efforts can encourage more work on quantum network protocols in the future. The paper discussed in the talk was published in ACM SIGCOMM conference 2020 and can be found at https://users.soe.ucsc.edu/~qian/papers/QuantumRouting.pdf.
Bio: Chen Qian is a Professor in the Department of Computer Science and Engineering at University of California Santa Cruz. He received his PhD from UT Austin, MPhil from HKUST, BSc from Nanjing University, all in computer science. He mainly works on some fundamental problems of computer networks, distributed systems, and security, including routing, forwarding, authentication, and system scalability. He received the NSF CAREER Award in 2018.
Time: Sep 1st, Thursday, 10:30 ET
10. Introduction Session Speaker: Yilian Liu
Ms student@Cornell University
Title: Solving Nonlinear Partial Differential Equations using Variational Quantum Algorithms on Noisy Quantum Computers
Abstract: Partial differential equations (PDEs) have long been the center of interest to system modeling in many disciplines of science and engineering, such as computational physics, fluid mechanics, and quantitative finance. However, as system sizes grow, PDEs, particularly nonlinear PDEs, quickly become intractable to solve using classical computation. Therefore, quantum computers are a natural candidate for solving large systems of equation as the number of grid points increases exponentially with the number of qubits. Variational quantum algorithms (VQA) have been proposed to solve nonlinear PDEs on noisy intermediate-scale quantum devices. In this talk, we will discuss some proposed approaches for solving nonlinear PDEs using VQA and the challenges of each approach, such as ansatz design and optimization. No prior knowledge of quantum computation is required.
Bio: Yilian Liu is currently an MS student in Applied and Engineering Physics at Cornell University. He has been designing and benchmarking variational quantum algorithms for solving nonlinear partial differential equations with Professor Peter McMahon. He also interned at the MIT Han Lab and worked on noise-aware quantum state preparation. He has recently joined Professor Karan Mehta’s group to work on trapped ion systems. Prior to studying at Cornell, he graduated from Reed College where he studied magnetic resonance of NV centers in diamond.
Time: Sep 1st, Thursday, 10:30 ET