Parameter-Dependent Noise Resilience in the Quantum Approximate Optimization Algorithm
The study investigates how choosing variational parameters in QAOA affects its stability against noise. It found that smaller, smoother parameters are more resilient to errors than large, erratic ones. The research provides a bound on error distance and suggests using predetermined or 'adiabatic-looking' parameter schedules for better noise resilience.