Mitigating Barren Plateaus in Variational Quantum Algorithms (Spring 2026)

Quantum computing is a developing approach to computation that utilizes principles of quantum mechanics, offering the potential for significantly faster processing than classical computation for certain types of problems. A leading candidate for near-term quantum computers are variational quantum algorithms. These algorithms tackle problems of optimization using both classical and quantum devices.

A major obstacle in the optimization process are regions of parameter space where gradients of the cost functions vanish exponentially, called Barren Plateaus. Our goals are to explore the construction of Variational Quantum Algorithms for specific optimization problems, including examples of problems with Barren Plateaus and methods for mitigating them. We also seek to understand how noise can induce Barren Plateaus and whether we can mitigate those.

For more information contact Julia Cen (juliacen@iastate.edu)

People:

• Julia Cen (Postdoc)

Pre-requisites:

• Knowledge of calculus, linear algebra and probability

• Programming skills (Python) are desired but not required