Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices

TL;DR

This paper investigates how quantum circuit cutting can mitigate noise effects in QAOA for MaxCut on NISQ devices, demonstrating improved solution quality through experiments on superconducting hardware.

Contribution

It provides empirical evidence that circuit cutting enhances QAOA performance on NISQ devices by reducing noise impact, a novel application in quantum optimization.

Findings

Circuit cutting reduces noise effects in QAOA.

Improved solution quality with circuit cutting on NISQ devices.

Experimental validation on superconducting hardware.

Abstract

Noisy Intermediate-Scale Quantum (NISQ) devices are restricted by their limited number of qubits and their short decoherence times. An approach addressing these problems is quantum circuit cutting. It decomposes the execution of a large quantum circuit into the execution of multiple smaller quantum circuits with additional classical postprocessing. Since these smaller quantum circuits require fewer qubits and gates, they are more suitable for NISQ devices. To investigate the effect of quantum circuit cutting in a quantum algorithm targeting NISQ devices, we design two experiments using the Quantum Approximate Optimization Algorithm (QAOA) for the Maximum Cut (MaxCut) problem and conduct them on state-of-the-art superconducting devices. Our first experiment studies the influence of circuit cutting on the objective function of QAOA, and the second evaluates the quality of results obtained by the whole algorithm with circuit cutting. The results show that circuit cutting can reduce the effects of noise in QAOA, and therefore, the algorithm yields better solutions on NISQ devices.