Practical demonstration of quantum approximate optimization on Google’s superconducting qubit processor

The quantum approximate optimization algorithm (QAOA) has attracted significant interest as an algorithm suitable for noisy, intermediate-scale quantum (NISQ) computers. QAOA seeks to find approximate ground states of classical Hamiltonians, typically representing binary optimization problems. We demonstrate a practical implementation of QAOA on three problem families at various qubit counts and circuit depths. We show good performance at larger problem sizes and on more complicated problems than has been previously reported. We investigate practical considerations for implementing near-term quantum algorithms. QAOA is a hybrid algorithm involving a classical “outer-loop” optimizer to find optimal parameters. We profile traditional and novel classical outer-loop optimizers to minimize algorithm run-time in a real-world scenario.