Quantum error mitigation in quantum annealing : General access Advantage2 kink-density and kink-correlators

Overview General access Advantage2 kink density and kink correlators as a function of anneal duration and coupling strength. This data is relevant to a second version of the paper "Quantum error mitigation in quantum annealing" currently under journal review, an earlier preprint with comparable data at smaller scale is here https://arxiv.org/abs/2311.01306 . A single file kink_density.csv demonstrates kink density for various anneal durations (ta) and coupling strengths (J).The kink value per-site per-read is k_i = (1 - s_i s_{i+1})/2, s_i denotes the state of spin i.Kink density is estimated as k = <k_i>, where <> denotes a per-programming site (i=0, .., 1023) and 1000-sample (read-out) average.Mean and variance are given with respect to repeated programmings (number of programmings is either 5 or 160 as indicated).See paper methods. For each ta and J relevant to the paper a csv is provided for kink correlations as a function of displacement r = 0, .., 1023.Kink correlations are estimated as C(r) = <k_i k_{i+r}>/k^2 - 1, where k is the kink density and <> denotes a per-programming site (i = 0 , .., 1023) and 1000-sample (read-out) average.Mean and variance are given with respect to 160 repeated programmings.See paper methods. Acknowledgements We would like to thank Gonzalo Alvarez, Daniel Lidar, Hidetoshi Nishimori and Marek Rams for fruitful discussions and comments on the manuscript. This research used resources from the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. Work at UBC was supported by the NSERC Alliance Quantum Program (Grant ALLRP-578555), CIFAR, and the Canada First Research Excellence Fund, Quantum Materials, and Future Technologies Program. The work of Jacek Dziarmaga was supported by the National Science Center (NCN), Poland, under project 2021/03/Y/ST2/00184 within the QuantERA II Program that has received funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement No 101017733.