Generating target graph couplings for the quantum approximate optimization algorithm from native quantum hardware couplings

Rajakumar, Joel; Moondra, Jai; Gard, Bryan T.; Gupta, Swati; Herold, Creston

doi:10.1103/physreva.106.022606

Cited by 3 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our method is based on an exact calculation of the quantum state and this scales exponentially in compute time and memory with the number of ions. We also considered a single MS interaction, while extensions to multiple MS interactions interleaved with other operations are necessary for modeling generic quantum circuits and QAOA instances [34]. The main bottleneck in applying our approach to deeper circuits is that tracking the evolution of the vibrational modes becomes more involved.…”

Section: Our Analysis In Tablementioning

confidence: 99%

“…QAOA has been a topic of contemporary research including experiments [16][17][18], theory [19][20][21][22][23], and simulations [24][25][26], with applications ranging from combinatorial optimization to quantum simulation [27][28][29]. In digital approaches to QAOA, it is expected that two-qubit gate error rates of 10 −5 − 10 −6 will be necessary for quantum advantage [30][31][32][33], while a recent proposal for QAOA with many-qubit MS interactions uses far shallower circuits in certain contexts [34], potentially avoiding the digital limitations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modelling noise in global Molmer-Sorensen interactions applied to quantum approximate optimization

Lotshaw¹,

Battles²,

Gard³

et al. 2022

Preprint

View full text Add to dashboard Cite

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 parameterizes these errors from simple experimental measurements, without free parameters. We validate the model in comparison with experiments that implement sequences of MS interactions on two and three 171 Yb + ions. The model shows good 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 0.92 +0.06 −0.06 , 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.

show abstract

Section: Our Analysis In Tablementioning

confidence: 99%