Building Practical Quantum Computing through Hardware-Aware Algorithm-Calibration Co-Design

Abstract:

Quantum supremacy experiments in 2019 and the recent release of the Willow quantum chip in 2024 have brought quantum computing into the spotlight. Decades of theoretical development have rigorously established a long-standing belief that quantum computing offers substantial computational speedups in areas such as scientific computing compared to classical electronic computers. However, challenges such as limited system size and high operation errors still impede the realization of general-purpose quantum computation. This dilemma pushes the next-generation quantum computing hardware and its quantum advantage into a corner, where the idealized computing paradigm clashes with the intractable quantum errors and limited quantum resources.

In this talk, I will share my research on strategies to design more accurate and scalable quantum computing and applications by navigating beyond this “quantum corner”. First, I will demonstrate how gradient-based and gradient-free optimizations can address critical challenges in quantum algorithm design, yielding state-of-the-art solutions in quantum scientific computing. Next, I will introduce how advanced signal processing and Fourier analysis enable high-precision and elegantly simple quantum calibration techniques, theoretically justified for their optimality and experimentally validated on Google’s superconducting platform. Lastly, I will discuss a novel paradigm for robust control of quantum computers. Through a hardware-aware algorithm-calibration co-design approach, we can bridge the gap between theoretical and experimental research, paving the way for practical quantum advantages in the near future. Furthermore, I will outline my broader research vision and highlight exciting future research directions aimed at fully unlocking the potential of quantum computation.

Bio:

Yulong Dong is currently a Research Scientist at ByteDance Research US (AI Lab) in San Jose, California. In 2023, he obtained his Ph.D. in Applied Mathematics from the University of California, Berkeley, advised by Professor Lin Lin and Professor K. Birgitta Whaley. Before that, he obtained his B.S. from the University of Science and Technology of China. During his Ph.D. studies, he completed two internships at Google’s Quantum AI Team. His research focuses on designing more accurate and scalable quantum computing and demonstrating quantum computational advantages. Working as both a theorist and an engineer, he brings engineering ideas to design resource-efficient and error-robust quantum algorithms, and tailors quantum hardware systems to the specification of applications. His work is broadly applied in various fields by researchers in both academia and industry. His research has resulted in publications in leading journals and conferences such as Nature Communications, PRX Quantum, and TQC Conference.