Improved success probability with greater circuit depth for the quantum approximate optimization algorithm

TL;DR

This paper demonstrates that increasing circuit depth in the quantum approximate optimization algorithm (QAOA) can improve success probability on small quantum processors, specifically solving instances of the exact-cover problem with high success rates.

Contribution

The study implements QAOA on a superconducting qubit platform and shows that greater circuit depth enhances success probability in solving combinatorial optimization problems.

Findings

Achieved 96.6% success probability on small quantum hardware.

Implemented QAOA up to level two on two superconducting qubits.

Demonstrated the potential of deeper circuits for near-term quantum algorithms.

Abstract

Present-day, noisy, small or intermediate-scale quantum processors---although far from fault-tolerant---support the execution of heuristic quantum algorithms, which might enable a quantum advantage, for example, when applied to combinatorial optimization problems. On small-scale quantum processors, validations of such algorithms serve as important technology demonstrators. We implement the quantum approximate optimization algorithm (QAOA) on our hardware platform, consisting of two superconducting transmon qubits and one parametrically modulated coupler. We solve small instances of the NP-complete exact-cover problem, with 96.6% success probability, by iterating the algorithm up to level two.