Quantum Architectures and Systems Workshop Program
Workshop registration is FREE but you must register in advance by August 1.
Student Posters are Invited. Limited travel support is available for best student poster presentations.
When: August 10, 2025
Where: DoubleTree by Hilton Lansing | 111 N Grand Ave | Lansing, Michigan
Co-located with the IEEE Midwest Symposium on Circuits and Systems (MWSCAS) 2025
What: Quantum computing offers transformative potential across fields like AI, optimization, and wireless communications by enabling exponential speedups over classical methods. However, challenges such as qubit noise, hardware limitations, and error correction remain significant barriers. This workshop explores advances in quantum machine learning and system architectures that aim to bridge the gap between current capabilities and real-world quantum applications. Four expert talks will highlight progress and opportunities in integrating quantum technologies into next-generation computing and network systems. A poster session on these topics will follow the talks, providing additional opportunities for discussion and engagement.
Organizers: Keshab K. Parhi, University of Minnesota; Rajiv Joshi, IBM T.J. Watson Research Center
9:00 - 10:30 | CSS Quantum Codes and Circuit Optimization
Keshab K. Parhi | Dept. of Electrical & Computer Eng.
University of Minnesota Twin Cities | Minneapolis, MN
Abstract
This talk will provide an overview of quantum error correcting codes (ECCs) and describe the developments of Calderbank-Shor-Steane (CSS) codes, and their quantum circuit optimizations. Specific topics include: (a) Introduction to quantum gates and circuits, (b) Shor’s 9-qubit ECC and stabilizer formalism for quantum ECCs: bit-flip and phase-flip codes, (c) Systematic method for construction of quantum ECC circuits: encoder, syndrome generator, and decoder circuits, (d) Optimization of quantum ECC circuits in terms of number of multiple-qubit gates: circuit equivalence rules and matrix equivalence, and (e) Nearest neighbor compliant (NNC) quantum ECC circuits.
Bio
Keshab K. Parhi is currently the Erwin A. Kelen Chair of Electrical Engineering in the Department of Electrical and Computer Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA.
He has authored or co-authored over 750 articles including 16 that have won best paper or best student paper awards, is the inventor of 36 patents, and has authored the textbook VLSI Digital Signal Processing Systems: Design and Implementation (Wiley, 1999). His current research addresses VLSI architectures for machine learning, hardware security, quantum codes, and data-driven neuroscience with focus on quantiative machine learning for neuro-psychiatric disorders. He is a recipient of numerous awards including the 2003 IEEE Kiyo Tomiyasu Technical Field Award, and the 2017 Mac Van Valkenburg Award and the 2012 Charles A. Desoer Technical Achievement Award from the IEEE Circuits and Systems Society. He served as the Editor-in-Chief of the IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS, Part-I during 2004 and 2005, and currently serves as the Editor-in-Chief for IEEE Circuits and Systems Magazine. He is a Fellow of IEEE, ACM, AAAS, NAI, and AIMBE.
11:00 - 12:30 | Quantum Machine Learning: Bridging Quantum Computing and Artificial Intelligence
Samuel Yen-Chi Chen | Lead Research Scientist, Wells Fargo
Abstract
Quantum Machine Learning (QML) stands at the cutting edge of computational intelligence, integrating quantum computing with classical machine learning to tackle complex problems beyond the reach of conventional methods. This talk will examine how QML harnesses quantum mechanical principles — including superposition, entanglement, and interference — to enable novel learning paradigms. Special emphasis will be placed on variational quantum circuits (VQCs) as a core building block for designing QML models on noisy intermediate-scale quantum (NISQ) hardware. In addition, we will introduce emerging techniques in Quantum Architecture Search (QAS), which automate the discovery and optimization of quantum circuit structures tailored for specific learning tasks. Drawing on our latest research, we will showcase applications where QML and QAS synergistically advance performance across multiple domains. The presentation will conclude by discussing the mutual reinforcement between artificial intelligence and quantum computing, outlining both the opportunities and key challenges that shape the future of QML.
Bio
Dr. Samuel Yen-Chi Chen received his Ph.D. and B.S. degrees in physics, as well as an M.D. degree in medicine, from National Taiwan University, Taipei, Taiwan. He is currently a Lead Research Scientist at Wells Fargo Bank, specializing in quantum machine learning (QML). Previously, he was an Assistant Computational Scientist at the Computational Science Initiative, Brookhaven National Laboratory. Dr. Chen pioneered the use of variational quantum circuits for deep reinforcement learning and is the inventor of quantum LSTM. His research interests focus on developing quantum machine learning algorithms and leveraging classical AI techniques to tackle quantum computing challenges, including quantum error correction and quantum architecture search. He is actively involved in privacy-preserving quantum AI research and is an experienced distributed computing researcher and developer. In 2019, he won First Prize in the Software Competition (Research Category) from Xanadu Quantum Technologies. As a seasoned speaker, Dr. Chen has delivered tutorials on quantum machine learning at top-tier conferences. He has presented on quantum neural networks for speech and natural language processing (IJCAI 2021, ICASSP 2022) and quantum tensor networks for signal processing (ICASSP 2024, IJCNN 2024, IEEE QCE 2024). At IEEE ICC 2024, he explored QML applications in 6G communication. Beyond major conferences, Dr. Chen is a frequent speaker at leading research seminars and industry meetups, including the ND-MIT Quantum Computer Systems Lecture Series, Washington DC Quantum Computing Meetup, AQT Seminars at Lawrence Berkeley National Lab, and the Quantum Information Summit at American University of Beirut. He has also presented at ICASSP, IEEE QCE, QTML, NeurIPS, ACM CIKM and IJCNN, covering a wide range of topics in quantum machine learning.
13:30 - 15:00 | Building a Hybrid Quantum-Classical Computing Ecosystem
Gokul Ravi | Computer Science & Engineering
University of Michigan | Ann Arbor, MI
Abstract
Quantum computing (QC) is a transformative technology with the potential to revolutionize computing. Despite major theoretical and experimental progress over the past three decades, a significant gap remains between the demands of quantum applications and the capabilities of current hardware. QC still faces major challenges in delivering accurate, efficient solutions to real-world problems. The quantum ecosystem is inherently hybrid, with quantum devices tightly coupled to classical hardware and software. Advancing these components in a synergistic manner is essential to bridging this need-capability gap and enabling a practical quantum future. As the field continues to grow, substantial progress is needed at the quantum-classical interface, including: (a) scalable software for executing real-world applications on noisy devices, (b) low-cost, efficient classical hardware with minimal latency and bandwidth limitations for scaling up quantum processing, and (c) a smooth transition path from noisy devices to fault-tolerant systems. In this talk, I will highlight several examples of our research addressing these challenges.
Bio
Gokul Ravi is an assistant professor of Computer Science and Engineering at the University of Michigan. His research focuses on the design of scalable quantum algorithms and architectures, with an emphasis on hybrid quantum-classical optimization, fault-tolerant quantum computing, and near-term quantum error mitigation. Dr. Ravi received his Ph.D. in Electrical and Computer Engineering from the University of Wisconsin–Madison in 2020 and subsequently held a postdoctoral position as an NSF CRA/CCC Computing Innovation Fellow at the University of Chicago from 2020 to 2023.
15:00 - 16:30 | Quantum and Quantum-Inspired Computation for NextG Wireless Baseband Processing
Prof. Kyle Jamieson
Princeton University
Abstract
For wireless network designers, user demand for increasing amounts of capacity continues to outpace supply, and while 5G has made progress, even higher-performance remains impractical in part because baseband algorithms are extremely computationally demanding: there is elasticity in the relationship between spectral efficiency and expended compute cycles. This line of work aims to transform the current research landscape by leveraging quantum computation to overcome previous computational limitations, enabling new levels of wireless network performance, with the eventual outcome of incorporating quantum computation into tomorrow's Next Generation standards. We have implemented a series of such designs on quantum annealer and gate model computers: I will touch on a Large MIMO detector, an LDPC decoder, and work on a Polar code decoder. Our experiments evaluate these systems on real and synthetic channel traces, showing that 30 μs of compute time can enable 48 user, 48 AP antenna BPSK MIMO detection at 20 dB SNR with a bit error rate of 10E-6. For LDPC decoding, our quantum decoder achieves a performance improvement over an FPGA based soft belief propagation LDPC decoder, by reaching a bit error rate of 10E−8 and a frame error rate of 10E-6 at an SNR gap of up to 3.5 dB. I will conclude with a roadmap for future progress in this area.
Bio
Kyle Jamieson is Professor of Computer Science, Associated Faculty in Electrical and Computer Engineering, and leads the Princeton Advanced Wireless Systems Laboratory (https://paws.princeton.edu) at Princeton University. His research focuses on mobile and wireless systems for sensing, localization, and communication, and on massively parallel classical, quantum, and optical computational structures for NextG wireless communications systems. He received the BS (Mathematics, Computer Science), MEng (Computer Science and Engineering), and PhD (Computer Science) degrees from the Massachusetts Institute of Technology. He is a Distinguished Member of the ACM and a Senior Member of the IEEE.
16:30 - 18:00 | Student Poster Presentations