ASU Professor Wins NSF Grant to Tackle Quantum Noise With Smarter Algorithms
Baoyu Zhou is developing optimization methods that work on today's imperfect quantum hardware instead of waiting for perfect machines.
Arizona State University Assistant Professor of Industrial Engineering Baoyu Zhou has secured a three-year National Science Foundation grant to solve one of quantum computing's most stubborn problems: making algorithms work reliably on hardware that fundamentally cannot run perfectly.
The research takes a contrarian approach. While tech giants pour resources into building ever-more-stable qubits, Zhou is betting that robust mathematics can outpace hardware improvements.
"The question is how we design more efficient, robust algorithms that can work despite the noise in today's quantum hardware," Zhou said.
Working With Imperfect Machines
Quantum computers remain notoriously fragile. Qubits—the quantum analog of classical bits—pick up environmental disturbances that introduce random errors throughout calculations. This "quantum noise" has kept most systems confined to laboratory demonstrations rather than practical applications.
Zhou, who teaches in ASU's School of Computing and Augmented Intelligence, is partnering with Xiu Yang, Associate Professor of Industrial and Systems Engineering at Lehigh University. Together, they're developing optimization algorithms that bake uncertainty tolerance into their core logic rather than treating noise as an afterthought.
Instead of waiting for hardware to mature—a timeline that remains uncertain—the team is creating mathematical methods that extract reliable results from current, imperfect systems.
Phoenix Quantum Ambitions
The timing aligns with regional economic development goals. Sethuraman Panchanathan, former NSF Director and ASU University Professor of Technology and Innovation, leads the Phoenix Quantum Strategy initiative to position the city as a national quantum technology hub.
"Baoyu's work is exactly the kind of foundational research that will help establish Phoenix as a global leader in quantum technologies," Panchanathan said.
The Phoenix Quantum Strategy represents a coordinated effort to attract quantum research talent and industry investment to Arizona. Zhou's NSF-funded project adds academic depth to that ecosystem.
Practical Applications Ahead
If successful, the noise-aware algorithms could accelerate quantum computing's transition from theoretical promise to working tool. The research targets applications in artificial intelligence, molecular design, and biotechnology—fields where optimization problems quickly outstrip classical computing capacity.
The three-year funding window gives Zhou's team time to develop scalable mathematical methods that account for hardware imperfections from the start. Rather than requiring error-free qubits, these algorithms would function reliably even when individual calculations drift or fail.
This approach could prove critical for near-term quantum advantage. Companies and researchers with access to current-generation quantum hardware may be able to solve meaningful problems sooner if their algorithms expect and accommodate noise rather than fighting it.
The work underscores a maturing field. Quantum computing research is shifting from proving concepts to engineering solutions that work within real-world constraints.