Performance of the Quantum Approximate Optimization Algorithm on the Maximum Cut Problem

The Quantum Approximate Optimization Algorithm (QAOA) is a promising approach for programming a near-term gate-based hybrid quantum computer to find good approximate solutions of hard combinatorial problems. However, little is currently know about the capabilities of QAOA, or of the difficulty of the requisite parameters optimization. We explore these issues with the aid of QuantumFlow, a simulation of a gate based quantum computer that uses TensorFlow to rapidly optimize variational quantum circuits. Our investigations support the prospects that QAOA will be an effective method for solving interesting problems on near-term quantum computers