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Quantum networks have experienced rapid advancements in both theoretical and experimental domains over the last decade, making it increasingly important to understand their large-scale features from the viewpoint of statistical physics. This review paper discusses a fundamental question: how can entanglement be effectively and indirectly (e.g., through intermediate nodes) distributed between distant nodes in an imperfect quantum network, where the connections are only partially entangled and subject to quantum noise? We survey recent studies addressing this issue by drawing exact or approximate mappings to percolation theory, a branch of statistical physics centered on network connectivity. Notably, we show that the classical percolation frameworks do not uniquely define the network’s indirect connectivity. This realization leads to the emergence of an alternative theory called “concurrence percolation”, which uncovers a previously unrecognized quantum advantage that emerges at large scales, suggesting that quantum networks are more resilient than initially assumed within classical percolation contexts, offering refreshing insights into future quantum network design.

The COVID‐19 pandemic drove a uniquely fervent pursuit to explore the potential of peptide, antibody, protein, and small‐molecule‐based antiviral agents against severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2). The interaction between the SARS‐CoV2 spike protein with the angiotensin‐converting enzyme 2 (ACE2) receptor that mediates viral cell entry was a particularly interesting target given its well‐described protein–protein interaction (PPI). This PPI is mediated by an α‐helical portion of ACE2 binding to the receptor binding domain (RBD) of the spike protein and thought to be susceptible to blockade through molecular mimicry. Small numbers of hydrocarbon‐stapled synthetic peptides designed to disrupt or block this interaction were tested individually and were found to have variable efficacy despite having related or overlapping sequences and similarly increased α‐helicity. Reasons for these differences are unclear and reported preclinical successes have been limited. This study sought to better understand reasons for these differences through evaluation of a comprehensive collection of hydrocarbon‐stapled peptides, designed based on four distinct principles: stapling position, number of staples, amino acid sequence, and primary sequence length. Surprisingly, we observed that the helicity and amino acid sequence iterations of hydrocarbon‐stapled peptides did not correlate with their bioactivity. Our results highlight the importance of iterative and combinatorial testing of these compounds to determine a configuration that best mimics natural binding and allows for chain flexibility while sacrificing structural helicity.