The promise of quantum computing is great.
The ability to solve in days problems in areas such as mathematics, finance and biological systems that are so complex that a classical computer would take hundreds of years to calculate.
But that capability is still a long way off, and noise is a big reason.
“The main obstacle for quantum computing is noise in the device, a hardware problem that causes errors in calculations,” says Xiu Yang, assistant professor of industrial and systems engineering at Lehigh University’s PC Rossin College of Engineering and Applied Science. “My research is at the algorithm level. And my goal is given this noise in the device, what can we do about it if we implement quantum algorithms?”
Yang recently received support from the National Science Foundation’s Faculty Early Career Development (CAREER) program – a $400,000 grant over five years – for his proposal to develop methods to model error propagation in quantum computing algorithms and that to filter resulting noise in the results.
The prestigious NSF CAREER Award is presented annually to young faculty members across the United States who exemplify the role of teaching fellows through outstanding research, excellence in education, and the integration of these two endeavors.
Yang will use state-of-the-art statistical and mathematical methods to quantify the uncertainty introduced by device noise in quantum computing algorithms. His work could help put quantum computing into practice in a variety of fields such as drug development, portfolio optimization and data encryption, where the technology is seen as a potential game-changer.
“My first goal is to model noise accumulation,” he says. “So, for example, when I run what is called an iterative algorithm, the noise or error of the device accumulates with each iteration. It is possible that with some algorithms the error is so large that the result of the algorithm is unusable. But in other cases it might not be that important.”
In these cases, the noise contaminating the result could be filtered out.
“So first I need to see how the bug is propagating and then, once I know how much it has skewed the result, I can determine if the results are useless or if the noise can be filtered out to get the desired result.” achieve,” he says.
To that end, Yang will examine different types of algorithms to see how they are affected and whether they need to be redesigned or if he can develop a filter instead.
“Basically, I analyze the suitability of quantum algorithms on quantum computers,” he says. “So this is a quantum numerical analysis from a probabilistic perspective.”
The ultimate goal is to enable quantum computing to fulfill its promise of unprecedented speed when it comes to solving highly complex problems, such as those physical and chemical systems that involve interactions between millions of molecules.
“Let’s say a pharmaceutical company wants to develop a new drug or vaccine,” he says. “You have to understand the interaction between all these particles. If I used a classic computer, this process would be very slow. But with a quantum computer it would be very, very fast.”
According to Yang, the award not only helps his field get a step closer to that reality, but also reflects recognition outside of his research community that the potential of quantum computers is worth the investment.
“This award comes from both NSF’s Computational and Communications Foundation Division and its Mathematical Sciences Division,” he says. “It means that people in the math and statistics community are now interested in quantum computing. They recognize that this is a very important area and we can make a contribution.”
About Xiu Yang
Xiu Yang is an assistant professor of industrial and systems engineering at Lehigh University. Before joining the faculty of the PC Rossin College of Engineering and Applied Science in 2019, he was a research scientist at the Pacific Northwest National Laboratory (PNNL).
Yang’s work focuses on modern scientific computing, including uncertainty quantification, multiscale modelling, physics-informed machine learning, and data-driven scientific discovery. He applies his methods in research areas such as fluid dynamics, hydrology, biochemistry, soft materials, climate modelling, energy storage and power grid systems.
His current focus is on quantum computing algorithms for scientific computing. His current focus is on quantum computing algorithms for scientific computing. He is a member of Lehigh’s Quantum Computing and Optimization Lab (QCOL) and affiliated with Lehigh’s Institute for Data, Intelligent Systems & Computation (I-DISC).
Yang received funding from the prestigious NSF Faculty Early Career Development (CAREER) Program in 2022 and received PNNL’s Outstanding Performance Award in 2015 and 2016. He was a 2019 member of the US Department of Energy’s Applied Mathematics Visioning Committee.
Yang received his PhD from the Department of Applied Mathematics at Brown University. He received his BS and MS from the Department of Scientific and Engineering Computing at Peking University in China.
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