Fig. 4: Quantum optimization of a five-variable QUBO problem on real quantum hardware.
From: Quantum approximate optimization via learning-based adaptive optimization
Measurement shots = 10,000. a, b show results from two circuit depths p = 1 and p = 2 QAOA, respectively. The line is the average optimization trajectory of five independent optimization trials, while the shaded area represents the standard deviation across five independent optimization trials. Averaged loss refers to the expectation value of the problem QUBO Hamiltonian. Raw (in orange color): at each step, we obtain the loss expectation directly from measurement results on the real quantum processor. Mitigation (in blue color): at each step, we obtain the loss expectation from measurement results integrated with QEM techniques. Ideal (in red color): at each step, we obtain the loss from numerical simulation. c The success ratio when we run inference on the trained QAOA program, i.e., the probability that we can obtain a correct bitstring answer for the problem on real quantum hardware. The dashed line is the random guess baseline with a probability of 1/16. We report the best success ratio of the five optimization trials.
