Fig. 1: The proof-of-concept workflow for error mitigated QAOA on the superconducting quantum processor with DARBO.
From: Quantum approximate optimization via learning-based adaptive optimization
We compile and deploy the 5-qubit QAOA program for given objective functions on a 20-qubit real superconducting quantum processor and evaluate the objective value with quantum error mitigation methods. DARBO treats the QEM-QAOA as a black-box, and optimizes the circuit parameters by fitting the surrogate model with constraints. The constraints are provided by the two adaptive regions, which are responsible for surrogate model building and acquisition function sampling, respectively.
