Researchers have made huge strides in developing qubits, the “quantum bits” that process data at the heart of quantum computers.
But computers are much more than their processors: It takes an entire “computing stack,” or layers of hardware and software systems, to make the machines useful.
As quantum computing matures, it will take an entirely new computing stack to turn the powerful qubits into practical, useable devices. Hezi Zhang, who started in fall 2026 as an assistant professor in electrical and computer engineering at the University of Wisconsin-Madison, is working to develop new computer architecture paradigms to harness qubit capabilities and lower demands on hardware to accelerate the pathway to practical quantum computing.
Zhang began her academic career studying physics at the University of Science and Technology of China in Hefei. During her studies, Zhang found herself drawn to the computational side of physics. That led her to earn a master’s degree in computer science from the Georgia Institute of Technology and a PhD from the University of California, San Diego, with a focus on the interplay between physics and computer architecture.
“Quantum computing is a very natural interest for me, given my background in both physics and computer science,” she says. “Much of our classical physics knowledge has been turned into practical use, but not really much of quantum physics. I think it would be great if we could turn quantum mechanics into practical things through quantum computing.”
At UW-Madison, Zhang hopes to bridge the gap between quantum hardware and upper-level software. Currently, there are many different types of qubits that each process information in different ways, including superconducting qubits, atomic qubits, photonic qubits and others. Each of these qubits has unique strengths and features that can be exploited by computer software.
However, current quantum programs and algorithms are written abstractly and are “qubit-neutral,” ignoring hardware features and constraints at the bottom of the computing stack. “Our role is to bridge this gap between application and hardware,” says Zhang. “We ask how you map the upper-level programs down to the hardware to make them more efficient and robust. We need the computer to consider these constraints and those patterns of operations that are supported by the hardware. Quantum computers can also be very ‘noisy’ if you don’t do any optimizations.”
Zhang hopes to add new research areas to her work as well, including heterogenous quantum computing. As the field progresses, it may turn out that some qubits are better at certain applications than others, making it necessary to create quantum computers that use more than one type of processor. Zhang hopes to study the best strategies for combining various types of qubit technologies and developing algorithms to handle these mixed systems.
She also plans to open a broader space for quantum error correction, an important element in the quantum computing stack that is a single discrete layer in current architectures. Zhang wants to investigate whether it is more efficient to spread the error correction throughout the stack. She also plans to investigate hybrid quantum and classical computing strategies for AI. It’s likely that quantum and classical computing will coexist, with quantum doing powerful physical system simulations and AI-empowered classical machines learning and analyzing that data. She hopes to create new paradigms that allow the systems to work together, managed by AI to solve difficult problems.
UW-Madison, Zhang says, is an ideal place for her research, with quantum research spread across ECE, physics, computer science and other departments. “I like the people and the ecosystem here,” she says. “There are many great researchers across different departments and a quantum institute where researchers from various backgrounds can work together.”
Photo of Hezi Zhang by Joel Hallberg