Quantum approximate optimization on a gate-model superconducting processor

The Quantum Approximate Optimization Algorithm (QAOA) [Farhi et al. arXiv:1411.4028] is a promising application for near-term quantum computing devices and has potential to demonstrate quantum supremacy [Farhi & Harrow arXiv:1602.07674]. We compile and run the QAOA algorithm on a programmable superconducting qubit processor, assessing its performance on instances of the MAX-CUT and k-coloring problems. Experimental performance is analyzed to measure the robustness of QAOA to device noise, and we comment on its outlook as a near-term demonstration of quantum supremacy.