.png){kind=icon}
Return
Quantum Annealing Initialization of the Quantum Approximate Optimization Algorithm
.png){kind=icon}
OA
AI
DOI：10.22331/q-2021-07-01-491.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The quantum approximate optimization algorithm (QAOA) is a prospective near-term quantum algorithm due to its modest circuit depth and promising benchmarks. However, an external parameter optimization required in the QAOA could become a performance bottleneck. This motivates studies of the optimization land-scape and search for heuristic ways of parameter initialization. In this work we visualize the optimization landscape of the QAOA applied to the MaxCut problem on random graphs, demonstrating that random initialization of the QAOA is prone to converging to local minima with sub-optimal performance. We introduce the initialization of QAOA parameters based on the Trotterized quantum annealing (TQA) protocol, parameterized by the Trotter time step. We find that the TQA initialization allows to circumvent the issue of false minima for a broad range of time steps, yielding the same performance as the best result out of an exponentially scaling number of random initializations. Moreover, we demonstrate that the optimal value of the time step coincides with the point of proliferation of Trotter errors in quantum annealing. Our results suggest practical ways of initializing QAOA protocols on near-term quantum devices and reveal new connections between QAOA and quantum annealing.
Journal
IF:
5.4
Papers:
974
Citations:
1.0W
Organization
Cited Papers
ShareSaveShareSaveOptimizing Variational Quantum Algorithms Using Pontryagin's Minimum Principle.png)
PHYSICAL REVIEW X
OAAI
PHYSICAL REVIEW XIF15.7
OAAIShareSaveFacile Synthesis, Characterization, Nanocrystal Growth and Photoluminescence Properties of GeS NanowiresNano
NanoIF0
PREAI
ShareSaveSclerosing Encapsulating Peritonitis: Early and Late Results of Surgical Management in 32 Cases
PREAI
ShareSave
View more