Abstract: With the end of Dennard scaling and the slowing of Moore’s law, improvements in general purpose 
computing have plateaued. In response, new paradigms are emerging at the frontiers of the 
computing spectrum, driving new opportunities for technological and societal advancement. At the 
low-power frontier, edge AI hardware enables ubiquitous, privacy-preserving intelligence with low 
latency and scalability. At the high-performance frontier, quantum computing is gaining practicality, 
offering solutions to previously intractable problems in physical sciences, biology, and 
optimization. Despite their differences, both domains share a common, critical challenge: the need 
for integrated circuits (ICs) that are simultaneously energy-efficient and compact. Edge nodes must 
operate under tight energy and form-factor constraints, while quantum-classical interfaces must 
meet stringent thermal and spatial limits at cryogenic temperatures.
In this talk, I will present cross-stack designs of low-power ICs that address these challenges at 
both frontiers of the computing spectrum. The first thrust focuses on a series of energy-efficient 
edge AI processors that co-optimize algorithm, architecture, circuit, and technology. The second 
highlights scalable cryogenic CMOS co-processing hardware for fault-tolerant quantum computing 
(FTQC), with emphasis on quantum error correction decoding and qubit readout. I will conclude 
with my vision for advancing both thrusts through a unified methodology grounded in the emerging 
paradigm of physical AI, and how AI-driven design automation can accelerate progress in both 
classical and quantum domains.

Bio: Qirui Zhang is a Research Investigator in the Department of Electrical and Computer Engineering at 
the University of Michigan, Ann Arbor, where he received his Ph.D. in 2024, advised by Prof. Dennis 
Sylvester. His research focuses on the cross-stack co-design of resource-efficient integrated 
circuits and systems with algorithm, architecture, and technology layers, targeting emerging 
computing paradigms such as edge AI and quantum computing. He is also broadly interested in 
computer-aided design for integrated circuits and quantum computers.
Qirui's research has been recognized with several honors, including the Best Paper Award at the 
2022 tinyML Research Symposium, Best Paper Award at IEEE ASAP 2023, Best Student Paper Award 
(1st Place) at IEEE ESSERC 2024, and a Best Student Paper Nomination at the 2025 Symposium on 
VLSI Technology and Circuits. He was also a finalist of the 2023 Qualcomm Innovation Fellowship 
(North America). He serves as a reviewer for the IEEE Journal of Solid-State Circuits and the IEEE 
Transactions on Circuits and Systems I. He received his B.S. in Microelectronics Science and 
Engineering from Shanghai Jiao Tong University in 2018