Quantum Computing System Lecture Series
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1. Research Session Speaker: Prineha Narang
Assistant Professor@Harvard University
Title: Building Blocks of Scalable Quantum Information Science
Abstract: Quantum information technologies are expected to enable transformative technologies with wide-ranging global impact. Towards realizing this tremendous promise, efforts have emerged to pursue quantum architectures capable of supporting distributed quantum computing, networks and quantum sensors. Quantum architecture at scale would consist of interconnected physical systems, many operating at their individual classical or quantum limit. Such scalable quantum architecture requires modeling that accurately describes these mesoscopic hybrid phenomena. By creating predictive theoretical and computational approaches to study dynamics, decoherence and correlations in quantum matter, our work could enable such hybrid quantum technologies1,2. Capturing these dynamics poses unique theoretical and computational challenges. The simultaneous contribution of processes that occur on many time and length-scales have remained elusive for state-of-the-art calculations and model Hamiltonian approaches alike, necessitating the development of new methods in computational physics3–5. I will show selected examples of our approach in ab initio design of active defects in quantum materials6–8, and control of collective phenomena to link these active defects9,10. Building on this, in the second part of my seminar, I will present promising physical mechanisms and device architectures for coupling (transduction) to other qubit platforms via dipole-, phonon-, and magnon-mediated interactions9–12. In a molecular context, will discuss approaches to entangling molecules in the strong coupling regime. Being able to control molecules at a quantum level gives us access to degrees of freedom such as the vibrational or rotational degrees to the internal state structure. Entangling those degrees of freedom offers unique opportunities in quantum information processing, especially in the construction of quantum memories. In particular, we look at two identical molecules spatially separated by a variable distance within a photonic environment such as a high-Q optical cavity. By resonantly coupling the effective cavity mode to a specific vibrational frequency of both molecules, we theoretically investigate how strong light-matter coupling can be used to control the entanglement between vibrational quantum states of both molecules. Linking this with detection of entanglement and quantifying the entanglement with an appropriate entanglement measure, we use quantum tomographic techniques to reconstruct the density matrix of the underlying quantum state. Taking this further, I will present some of our recent work in capturing non-Markovian dynamics in open quantum systems (OQSs) built on the ensemble of Lindblad’s trajectories approach 13–16. Finally, I will present ideas in directly emulating quantum systems, particularly addressing the issues of model abstraction and scalability, and connect with the various quantum algorithm efforts underway.
Bio: Prineha Narang is an Assistant Professor at the John A. Paulson School of Engineering and Applied Sciences at Harvard University. Prior to joining the faculty, Prineha came to Harvard as a Ziff Environmental Fellow at the Harvard University Center for the Environment. She was also a Research Scholar in Condensed Matter Theory at the MIT Dept. of Physics, working on new theoretical methods to describe quantum interactions. Prineha’s work has been recognized by many, including the Mildred Dresselhaus Prize, a Friedrich Wilhelm Bessel Research Award (Bessel Prize) from the Alexander von Humboldt Foundation, a Max Planck Sabbatical Award from the Max Planck Society, and the IUPAP Young Scientist Prize in Computational Physics in 2021, a National Science Foundation CAREER Award in 2020, being named a Moore Inventor Fellow by the Gordon and Betty Moore Foundation for innovations in quantum science and technology, CIFAR Azrieli Global Scholar by the Canadian Institute for Advanced Research, a Top Innovator by MIT Tech Review (MIT TR35), and a Young Scientist by the World Economic Forum in 2018. In 2017, she was named by Forbes Magazine on their “30under30” list for her work in atom-by-atom quantum engineering.
Sep 15th (Reschedule to Oct 6th), Thursday, 10:30 ET
2. Research Session Speaker: Jakub Szefer
Asscoiate Professor@Yale University
Title: Quantum Computer Hardware Cybersecurity
Abstract: As Quantum Computer device research continues to advance rapidly, there are also advances at the other levels of the computer system stack that involve these devices. In particular, more and more of the Quantum Computer devices are becoming available as cloud-based services through IBM Quantum, Amazon Braket, Microsoft Azure, and others. In parallel, researchers have put forward ideas about multi-programming of the Quantum Computer devices where single device can be shared by multiple programs, or even multiple users. While all of the advances make the Quantum Computer devices more easily accessible and increase utilization, they open up the devices to various security threats. Especially, with cloud-based access and multi-tenancy, different, remote, and untrusted users could abuse the Quantum Computer devices to leak information from other users using the shared devices, map or learn about the Quantum Computer infrastructure itself. Malicious users could also try to reverse engineer the Quantum Computer architectures to learn about the design of the hardware devices. On the other hand, users are not immune today from malicious or compromised cloud operators who may want to spy on the circuits of the users which are executing on the Quantum Computers hosted within the operator’s data centers. Considering the different security threats, and lessons learned from security of classical computers, this talk will introduce the new research field of Quantum Computer Hardware Security, present recent research results in attacks and defenses on Quantum Computers. The goal of the presentation is to motivate discussion about Quantum Computer Hardware Cybersecurity and make connections between the Quantum Computer research community and the Hardware Security research community to help develop secure Quantum Computer architectures and protect the devices before they are widely deployed.
Bio: Jakub Szefer’s research focuses on computer architecture and hardware security. His research encompasses secure processor architectures, cloud security, FPGA attacks and defenses, hardware FPGA implementation of cryptographic algorithms, and most recently quantum computer cybersecurity. His research is supported through National Science Foundation and industry grants and donations. He is currently an Associate Professor of Electrical Engineering at Yale University, where he leads the Computer Architecture and Security Laboratory (CASLAB). Prior to joining Yale, he received Ph.D. and M.A. degrees in Electrical Engineering from Princeton University, and B.S. degree with highest honors in Electrical and Computer Engineering from University of Illinois at Urbana-Champaign. He has received the NSF CAREER award in 2017. Jakub is the author of first book focusing on processor architecture security: “Principles of Secure Processor Architecture Design”, published in 2018. Recently, he has been promoted to the IEEE Senior Member rank in 2019 and is a recipient of the 2021 Ackerman Award for Teaching and Mentoring. Details of Jakub’s research and projects can be found at: https://caslab.csl.yale.edu/~jakub
Time: Sep 22, Thursday, 10:30 ET
3. Research Session Speaker: Guan Qiang
Assistant Professor@Kent State University
Title: Enabling robust quantum computer system by understanding errors from NISQ machines
Abstract: The growth of the need for quantum computers in many domains such as machine learning, numerical scientific simulation, and finance has urged quantum computers to produce more stable and less error-prone results. However, mitigating the impact of the noise inside each quantum device remains a present challenge. In this project, we utilize the system calibration data collected from the existing IBMQ machines, applying fidelity degradation detection to generate the fidelity degradation matrix. Based on the fidelity degradation matrix, we define multiple new evaluation metrics to compare the fidelity between the qubit topology of the quantum machines fidelity of qubits on the same topology, and to search for the most error-robust machine so that users can expect the most accurate results and study the insight of correlation between qubits that may further motivate the quantum compiler design for the qubit mapping. Besides, we build a visualization system VACSEN to illustrate the errors and reliability of the quantum computing backend.
Bio: Dr. Qiang Guan is an assistant professor in the Department of Computer Science at Kent State University, Kent, Ohio. Dr. Guan is the director of the Green Ubiquitous Autonomous Networking System lab (GUANS). He is also a member of the Brain Health Research Institute (BHRI) at Kent State University. He was a computer scientist at Los Alamos National Laboratory before joining KSU. His current research interests include: fault tolerance design for HPC applications; HPC-Cloud hybrid systems; virtual reality; quantum computing systems and applications.
Time: Sep 29, Thursday, 10:30 ET
4. Research Session Speaker: Bochen Tan
Title: Compilation for Near-Term Quantum Computing: Gap Analysis and Optimal Solution
Abstract: The most challenging stage in compilation for near-term quantum computing is qubit mapping, also called layout synthesis, where qubits in quantum programs are mapped to physical qubits. In order to understand the quality of existing solutions, we apply the measure-improve methodology, which has been successful in classical circuit placement, to this problem. We construct quantum mapping examples with known optimal, QUEKO, to measure the optimality gaps of leading heuristic compilers. On the revelation of large gaps, we set out to close them with optimal layout synthesis for quantum computing, OLSQ, a more efficient formulation of the qubit mapping problem into mathematical programming. We accelerate OLSQ with the transition mode and expand its solution space with domain-specific knowledge on applications like quantum approximate optimization algorithm, QAOA.
Bio: Bochen Tan received the B.S. degree in electrical engineering from Peking University in 2019, and the M.S. degree in computer science from University of California, Los Angeles in 2022. He is currently a graduate student researcher at UCLA focusing on design automation for quantum computing.
Time: Oct 13, Thursday, 10:30 ET
5. Research Session Speaker: Zeyuan Zhou
Title: Quantum Crosstalk Robust Quantum Control
Abstract: The prevalence of quantum crosstalk in current quantum devices poses challenges to achieving high-fidelity quantum logic operations and reliable quantum processing. Through quantum control theory, we develop an analytical condition for achieving crosstalk-robust single-qubit control of multi-qubit systems. We examine the effects of quantum crosstalk via a cumulant expansion approach and develop a condition to suppress the leading order contributions to the dynamics. The efficacy of the condition is illustrated in the domains of quantum state preservation and noise characterization through the development of crosstalk-robust dynamical decoupling (DD) and quantum noise spectroscopy (QNS) protocols. Using the IBM Quantum Experience superconducting qubits, crosstalk-robust state preservation is demonstrated on 27 qubits, where a 3× improvement in coherence decay is observed for single-qubit product and multipartite entangled states. Through the use of noise injection, we experimentally demonstrate the first known parallel crosstalk-robust dephasing QNS on a seven-qubit processor, where a 10^4 improvement in reconstruction accuracy over “cross-susceptible” alternatives is found. Together, these experiments highlight the significant impact the crosstalk mitigation condition can have on improving multi-qubit characterization and control on current quantum devices. In this talk, I will go through the theoretical framework we leveraged which enables the co-suppression of quantum crosstalk and system-environment noise. For the second part, I will discuss a wide range of applications on near-term devices from physical layer control and characterization to robust algorithms design and logical encoding.
Bio: Zeyuan(Victor) Zhou is a graduate student and a research assistant at Dr. Gregory Quiroz’s group at Johns Hopkins University. He is the recipient of the Dean’s fellowship at the G.W.C. Whiting School of Engineering. His primary research interests include theoretical quantum control, quantum error mitigation, and robust quantum algorithms. Victor has been working on devising general control criteria to suppress quantum crosstalk noise prevailing in current quantum technologies. The technique is broadly applied to different layers of quantum software stacks and enables robust and scalable quantum information processing. He received his B.S. in Physics and B.S. in Applied Mathematics and Statistics also from Johns Hopkins University.
Time: Oct 20, Thursday, 10:30 ET
6. Research Session Speaker: Wei Tang
Title: Distributed Quantum Computing
Abstract: Quantum processing units (QPUs) have to satisfy highly demanding quantity and quality requirements on their qubits to produce accurate results for problems at useful scales. Furthermore, classical simulations of quantum circuits generally do not scale. Instead, quantum circuit cutting techniques cut and distribute a large quantum circuit Into multiple smaller subcircuits feasible for less powerful QPUs. However, the classical post-processing incurred from the cutting introduces runtime and memory bottlenecks. We present TensorQC, which addresses the bottlenecks via novel algorithmic techniques including (1) a State Merging framework that locates the solution states of large quantum circuits using a linear number of recursions; (2) an automatic solver that finds high-quality cuts for complex quantum circults2x larger than prior works; and (3) a tensor network based post-processing that minimizes the classical overhead by orders of magnitudes over prior parallelization techniques. Our experiments reduce the quantum area requirement by at least 60% over the purely quantum platforms. We also demonstrated benchmarks up to 200 qubits on a single GPU, much beyond the reach of the strictly classical platforms.
Bio: Wei Tang is a fourth year Computer Science Ph.D. student at Princeton University in Professor Margaret Martonosi‘s group. His research interests include but not limited to Quantum Computing Architecture, and Machine Learning X Quantum Computing. Previously, He worked with Professor Jungsang Kim at Duke University on ion trapping experiments, and James B. Duke Professor Alfred Goshaw at Duke University in the field of high energy physics.
Time: Oct 27, Thursday, 10:30 ET
7. Research Session Speaker: Prof. Tirthak Patel
Incoming Assitant Professor at Rice University
Title: Developing Robust System Software Support for Quantum Computers
Abstract: The field of quantum computing has observed extraordinary advances in the last decade, including the design and engineering of quantum computers with more than a hundred qubits. While these engineering advances have been celebrated widely, computational scientists continue to struggle to make meaningful use of existing quantum computers. This is primarily because quantum computers suffer from prohibitively high noise levels, which lead to erroneous program outputs and limit the practical usability of quantum computers. Researchers and practitioners are actively devising theoretical and quantum hardware-based error mitigation techniques for quantum computers; while these efforts are useful, they do not help us realize the full potential of quantum computers. In this talk, I will discuss a unique opportunity space for improving performance and fidelity of quantum programs from a system software perspective. In particular, I will demonstrate how to carefully design novel system software solutions that can further the reach of hardware-only solutions and improve the usability of quantum computers.
Bio: Tirthak Patel is an incoming Assistant Professor at the Rice University Department of Computer Science as part of the Rice Quantum Initiative. He is currently a Computer Engineering Ph.D. Candidate at Northeastern University conducting systems level research at the intersection of quantum computing and high-performance computing (HPC). His research explores the trade-offs among factors affecting reliability, performance, and efficiency, in recognition of which I have received the ACM-IEEE CS George Michael Memorial HPC Fellowship and the NSERC Alexander Graham Bell Canada Graduate Scholarship (CGS D-3).
Time: Nov 03, Thursday, 10:30 ET
8. Research Session Speaker: Prof. Gushu Li
Incoming Assistant Professor at the University of Pennsylvania
Title: Enabling Deeper Quantum Compiler Optimization at High Level
Abstract: A quantum compiler is one essential and critical component in a quantum computing system to deploy and optimize the quantum programs onto the underlying physical quantum hardware platforms. Yet, today’s quantum compilers are still far from optimal. One reason is that most optimizations in today’s quantum compilers are local program transformations over very few qubits and gates. In general, it is highly non-trivial for a compiler that runs on a classical computer to automatically derive large-scale program optimizations at the gate-level.In this talk, we will discuss how we can systematically enhance the quantum compilers by introducing high-level program optimizations in the quantum software/compiler infrastructure. Instead of optimizing the quantum programs at the gate level, we design new quantum programming language primitives and intermediate representations that can maintain high-level properties of the programs. These high-level properties can then be leveraged to derive new large-scale quantum compiler optimizations beyond the capabilities of gate-level optimizations. In particular, we will introduce optimizing quantum simulation programs over a Pauli string based intermediate representation, mapping surface code onto superconducting architectures, and quantum program testing/error mitigation through projection-based quantum assertions. We believe that the high-level optimization approach can also be applicable to other quantum application domains and algorithmic properties.
Bio: Mr. Gushu Li is an incoming Assistant Professor at the Department of Computer and Information Science, University of Pennsylvania. He is currently a Ph.D. candidate at the University of California, Santa Barbara, advised by Prof. Yuan Xie and Prof. Yufei Ding. His research features the emerging quantum computer system and spans mainly across the quantum programming language, quantum compiler, and quantum computer architecture. His research has been recognized by the ACM SIGPLAN Distinguished Paper Award at OOPSLA 2020 and an NSF Quantum Information Science and Engineering Network Fellow Grant Award. His research outputs have been adopted by several industry/academia quantum software frameworks, including IBM’s Qiskit, Amazon’s Braket, Quantinuum’s t|ket>, and Oak Ridge National Lab’s qcor.
Time: Nov 10, Thursday, 10:30 ET
9. Research Session Speaker: Prof. Nai-Hui Chia
Assistant Professor at the Rice University
Title: Classical Verification of Quantum Depth
Abstract: Verifying if a remote server has sufficient quantum resources to demonstrate quantum advantage is a fascinating question in complexity theory as well as a practical challenge. One approach is asking the server to solve some classically intractable problem, such as factoring. Another approach is the proof of quantumness protocols. These protocols enable a classical client to check whether a remote server can complete some classically intractable problem and thus can be used to distinguish quantum from classical computers. However, these two approaches mainly focus on distinguishing quantum computers from classical ones. They do not directly translate into ones that separate quantum computers with different quantum resources. In this talk, we want to go one step further by showing protocols that can distinguish machines with different quantum depths. We call such protocols Classical Verification of Quantum Depth (CVQD). Roughly speaking, if a server has quantum circuit depth at most d, the classical client will reject it; otherwise, the classical client will accept it. Note that a malicious server, in general, can use classical computers to cheat. Thus, CVQD protocols shall be able to distinguish hybrid quantum-classical computers with different quantum depths. We will see two CVQD protocols: the first protocol can separate hybrid quantum-classical computers with quantum depth d and d+c (for c some fixed constant) assuming quantum LWE, and the second protocol is a two-prover protocol that achieves sharper separation (d versus d+3).
Bio: Nai-Hui Chia is an Assistant Professor in the Department of Computer Science at Rice University. Before that, he was an Assistant Professor in the Luddy School of Informatics, Computing, and Engineering at Indiana University Bloomington from 2021 to 2022, a Hartree Postdoctoral Fellow in the Joint Center for Quantum Information and Computer Science (QuICS) at the University of Maryland from 2020 to 2021, supervised by Dr. Andrew Childs, and a Postdoctoral Fellow at UT Austin from 2018 to 2020, working under the supervision of Dr. Scott Aaronson. he received my Ph.D. in Computer Science and Engineering at Penn State University, where he was fortunate to have Dr. Sean Hallgren as his advisor.
Time: Nov 17, Thursday, 10:30 ET
10. Research Session Speaker: Prof. Mohsen Heidari
Assistant Professor at the Indiana University, Bloomington
Title: Learning and Training in Quantum Environments
Abstract: Quantum computing presents fascinating new opportunities for various applications, including machine learning, simulation, and optimization. Quantum computers (QCs) are expected to push beyond the limits established by the classical laws of physics and surpass the capabilities of classical supercomputers. They leverage quantum-mechanical principles such as superposition and entanglement for computation, information processing, and pattern recognition. Superposition allows a system to exist in multiple states (until measurement). Entanglement facilitates non-local statistical correlations that classical models cannot produce since they violate Bell Inequalities. With such unique features, not only quantum advantage is on the horizon, but also a far greater capability to learn patterns from inherently quantum data by directly operating on quantum states of physical systems (e.g., photons or states of matter). Utilizing quantum data provides the ability to comprehend better, predict, and control quantum processes and opens doors to a wide range of applications in drug discovery, communications, security, and even human cognition. The first part of this talk covers an introduction to foundational concepts in quantum computing. The second part focuses on learning using near-term quantum computers for classical and quantum data. Mainly, I discuss the training of quantum neural networks (QNNs) using quantum-classical hybrid loops. I present some of the unique challenges in quantum learning due to effects such as the no-cloning principle, measurement incompatibility, and stochasticity of quantum. Then, I introduce a few solutions to address such challenges, particularly one-shot gradient-based training of QNNs suitable for near-term quantum computers with minimal qubit processing power. Lastly, I discuss applications of QNNs in the classification of quantum states, e.g., entanglement versus separability of qubits.
Bio: Mohsen Heidari is an Assistant Professor in the Department of Computer Science at the Luddy School of Informatics, Computing, and Engineering at Indiana University, Bloomington. He is a member of the NSF Center for Science of Information and Indiana University Quantum Science and Engineering Center (QSEc). He obtained his Ph.D. in Electrical Engineering in 2019 and his M.Sc. in Mathematics in 2017, both from the University of Michigan, Ann Arbor. Mohsen’s research interests lie in theoretical machine learning, quantum computing and algorithms, and classical and quantum information theory.
Time: Dec 01, Thursday, 10:30 ET