Modeling noise in global Mølmer-Sørensen interactions applied to quantum approximate optimization

• Published in: Physical review. A 2023-06, Vol.107 (6), Article 062406
• Main Authors: Lotshaw, Phillip C., Battles, Kevin D., Gard, Bryan, Buchs, Gilles, Humble, Travis S., Herold, Creston D.
• Format: Article
• Language: English
• Subjects: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; quantum gates; quantum information with trapped ions
• ISSN: 2469-9926; 2469-9934
• DOI: 10.1103/PhysRevA.107.062406

Many-qubit Mølmer-Sørensen (MS) interactions applied to trapped ions offer unique capabilities for quantum information processing, with applications including quantum simulation and the quantum approximate optimization algorithm (QAOA). Here, we develop a physical model to describe many-qubit MS interactions under four sources of experimental noise: vibrational mode frequency fluctuations, laser power fluctuations, thermal initial vibrational states, and state preparation and measurement errors. The model parametrizes these errors from simple experimental measurements, without free parameters. Herein, we validate the model in comparison with experiments that implement sequences of MS interactions on two 171Yb+ ions. The model shows reasonable agreement after several MS interactions as quantified by the reduced chi-squared statistic $χ^{2}_{red}$≈2. As an application we examine MaxCut QAOA experiments on three and six ions. The experimental performance is quantified by approximation ratios that are 91% and 83% of the optimal theoretical values. Our model predicts $0.93^{+0.03}_{-0.02}$ and $0.95^{+0.04}_{-0.03}$, respectively, with disagreement in the latter value attributable to secondary noise sources beyond those considered in our analysis. With realistic experimental improvements to reduce measurement error and radial trap frequency variations, the model achieves approximation ratios that are 99% of the optimal. Incorporating these improvements into future experiments is expected to reveal new aspects of noise for future modeling and experimental improvements.