Search

We introduce a framework for fault-tolerant post-selection (FTPS) of fault-tolerant codes and channels -- such as those based on surface-codes -- using soft-information metrics based on visible syndrome and erasure information. We introduce several metrics for ranking configurations of syndromes and erasures. In particular, we introduce the \emph{logical gap} (and variants thereof) as a powerful soft-information metric for predicting logical error rates of fault-tolerant channels based on topological error-correcting codes. The logical gap is roughly the unsigned weight difference between inequivalent logical corrections and is adaptable to any tailored noise model or decoder. We deploy this framework to prepare high-quality surface code magic states with low overhead under a model of independent and identically distributed (\emph{i.i.d.}) Pauli and erasure errors. Post-selection strategies based on the logical gap can suppress the encoding error rate of a magic state preparation channel to the level of the physical error rate with low overhead. For example, when operating at $60\%$ the bulk threshold of the corresponding surface code, an overall reduction of the encoding error rate by a factor of $15$ is achievable with a relative overhead factor of ${< 2}$ (approximately $23$ times less than that of simple syndrome-counting rules). We analyze a schematic buffer architecture for implementing post-selection rules on magic state factories in the context of magic state distillation. The FTPS framework can be utilized for mitigating errors in more general fault-tolerant logical channels., 10+7 pages, 13 figures, comments welcome

Background As part of our outpatient Antimicrobial Stewardship Program, we do surveillance of diagnoses and antibiotic use for common upper respiratory tract infections such as acute upper respiratory tract infection, acute bronchitis, sinusitis, and pharyngitis. We sought to evaluate the impact of the Covid-19 pandemic on the incidence rate of upper respiratory tract infection diagnoses per clinic visit during October 2020 to March 2021 season compared to the three prior respiratory seasons. We also sought to reflect of impact of increase in televisits and overlapping symptoms of COVID 19 and upper respiratory tract infections. Methods Our cohort study extending from October 2017 to March 2021. We collected number of diagnoses of upper respiratory infections and number of unique clinic visits during four consecutive respiratory seasons at our primary care sites via electronic health records. Results During the recent October 2020 to March 2021 respiratory season which coincided with the second NYC Covid-19 wave, we had 11569 unique clinic visits and 39 diagnoses of an upper respiratory tract infection - incident rate of 1.29. In the three prior respiratory seasons combined, we had 40939 unique clinic visits and 833 diagnoses of an upper respiratory tract infection – incident rate of 1.49. The incident rates showed a dramatic decline using the test based method and the chi square-statistic p< 0.0001 with an incident rate ratio using a poisson exact method of 6.0359. Statistical comparisons of the current season to each prior individual season yielded similar results. The percentage of Tele-visits during the current season was 19% compared to 0% in the 3 prior seasons. Conclusion During the first respiratory season from October 2020 to March 2021 in midst of the Covid-19 pandemic which also coincided with the second Covid-19 wave in New York, we saw a statistically significant decrease in incidence of common upper respiratory tract infection diagnoses per clinic visit compared to the three prior respiratory seasons. Overlapping signs and symptoms of upper respiratory tract infections and Covid-19 with the added percentage in Tele-visits did not cause an increase in incidence rates of upper respiratory tract infection diagnoses. Covid-19 related mitigation efforts may have played a role. Disclosures All Authors: No reported disclosures