Regensburg 2022 – wissenschaftliches Programm
QI 12.7: Vortrag
Donnerstag, 8. September 2022, 16:45–17:00, H8
Resilience of quantum approximate optimization against correlated errors — Joris Kattemölle and •Guido Burkard — Universität Konstanz, Konstanz, Deutschland
The Quantum Approximate Optimization Algorithm (QAOA) has the potential of providing a quantum advantage in large-scale optimization problems, as well as in finding the ground state of spin glasses. This algorithm is especially suited for Noisy Intermediate Scale Quantum (NISQ) devices because of its noise resilience. So far, this noise resilience has only been studied under the assumption of uncorrelated noise. However, in recent years, it has become increasingly clear that the noise impacting NISQ devices is significantly correlated. In this work, we introduce a model for both spatially and temporally (non-Markovian) correlated errors that allows for the independent variation of the marginalized local error probability and the correlation strength. Using this model, we study the effects of noise correlations on QAOA by full density matrix simulation. We find evidence that the performance of QAOA improves as the strength of noise correlations is increased at fixed marginalised local error probability. This shows that, as opposed to algorithms for fully error-corrected quantum computers, noise correlations need not be detrimental for NISQ algorithms such as QAOA, and may actually improve the performance thereof.