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Paper 1

Exploring the Role of Heavy Metals and Their Derivatives on the Pathophysiology of COVID-19.

Bahrami A, Arabestani MR, Taheri M, Farmany A, Norozzadeh F, Hosseini SM, Nozari H, Nouri F

Year

2022

Journal

Biological trace element research

DOI

10.1007/s12011-021-02893-x

arXiv

-

Many aspects of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its disease, COVID-19, have been studied to determine its properties, transmission mechanisms, and pathology. These efforts are aimed at identifying potential approaches to control or treat the disease. Early treatment of novel SARS-CoV-2 infection to minimize symptom progression has minimal evidence; however, many researchers and firms are working on vaccines, and only a few vaccines exist. COVID-19 is affected by several heavy metals and their nanoparticles. We investigated the effects of heavy metals and heavy metal nanoparticles on SARS-CoV-2 and their roles in COVID-19 pathogenesis. AgNPs, AuNPs, gold-silver hybrid NPs, copper nanoparticles, zinc oxide, vanadium, gallium, bismuth, titanium, palladium, silver grafted graphene oxide, and some quantum dots were tested to see if they could minimize the severity or duration of symptoms in patients with SARS-CoV-2 infection when compared to standard therapy.

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Paper 2

Toward Uncertainty-Aware and Generalizable Neural Decoding for Quantum LDPC Codes

Xiangjun Mi, Frank Mueller

Year

2025

Journal

arXiv preprint

DOI

arXiv:2510.06257

arXiv

2510.06257

Quantum error correction (QEC) is essential for scalable quantum computing, yet decoding errors via conventional algorithms result in limited accuracy (i.e., suppression of logical errors) and high overheads, both of which can be alleviated by inference-based decoders. To date, such machine-learning (ML) decoders lack two key properties crucial for practical fault tolerance: reliable uncertainty quantification and robust generalization to previously unseen codes. To address this gap, we propose \textbf{QuBA}, a Bayesian graph neural decoder that integrates attention to both dot-product and multi-head, enabling expressive error-pattern recognition alongside calibrated uncertainty estimates. Building on QuBA, we further develop \textbf{SAGU }\textbf{(Sequential Aggregate Generalization under Uncertainty)}, a multi-code training framework with enhanced cross-domain robustness enabling decoding beyond the training set. Experiments on bivariate bicycle (BB) codes and their coprime variants demonstrate that (i) both QuBA and SAGU consistently outperform the classical baseline belief propagation (BP), achieving a reduction of on average \emph{one order of magnitude} in logical error rate (LER), and up to \emph{two orders of magnitude} under confident-decision bounds on the coprime BB code $[[154, 6, 16]]$; (ii) QuBA also surpasses state-of-the-art neural decoders, providing an advantage of roughly \emph{one order of magnitude} (e.g., for the larger BB code $[[756, 16, \leq34]]$) even when considering conservative (safe) decision bounds; (iii) SAGU achieves decoding performance comparable to or even outperforming QuBA's domain-specific training approach.

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