Two-Qubit Implementation of QAOA for MAX-CUT on an NV-Center Quantum Processor

TL;DR

This paper demonstrates a proof-of-principle implementation of the QAOA algorithm for MAX-CUT on a two-qubit NV-center quantum processor, showcasing the platform's potential for quantum optimization tasks.

Contribution

It presents the first implementation of QAOA for MAX-CUT on an NV-center quantum processor, including methods to reconstruct computational basis populations from fluorescence signals.

Findings

Successfully implemented a single-layer QAOA on NV centers.

Reconstructed computational basis populations from fluorescence signals.

Established a baseline for future quantum optimization experiments.

Abstract

We report a proof-of-principle implementation of the quantum approximate optimization algorithm (QAOA) for the smallest nontrivial MAX-CUT instance on an NV-center-based quantum processor operating at room temperature. The two-qubit register is encoded in the electron spin and the ${}^{14}\mathrm{N}$ nuclear spin of a single NV$^-$ center. Using a minimization formulation of MAX-CUT, we implement a single-layer QAOA ansatz with native entangling and single-qubit control operations. Because the optical readout of the NV$^-$ center is not projective in the computational basis, we reconstruct computational-basis populations from averaged fluorescence signals and use them to determine the experimental QAOA cost landscape by scanning the variational parameters. These results show that the core elements of QAOA can be realized on this platform and establish a baseline for future improvements in phase tracking, coherence-preserving control, and scaling to larger problem sizes.