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Quantum adiabatic optimization seeks to solve combinatorial problems using quantum dynamics, requiring the Hamiltonian of the system to align with the problem of interest. However, these Hamiltonians are often incompatible with the native constraints of quantum hardware, necessitating encoding strategies to map the original problem into a hardware-conformant form. While the classical overhead associated with such mappings is easily quantifiable and typically polynomial in problem size, it is much harder to quantify their overhead on the quantum algorithm, e.g., the transformation of the adiabatic timescale. In this work, we address this challenge on the concrete example of the encoding scheme proposed in [Nguyen , PRX Quantum , 010316 (2023)], which is designed to map optimization problems on arbitrarily connected graphs into Rydberg atom arrays. We consider the fundamental building blocks underlying this encoding scheme and determine the scaling of the minimum gap with system size along adiabatic protocols. Even when the original problem is trivially solvable, we find that the encoded problem can exhibit an exponentially closing minimum gap. We show that this originates from a quantum coherent effect, which gives rise to an unfavorable localization of the ground-state wave function. On the QuEra Aquila neutral atom machine, we observe such localization and its effect on the success probability of finding the correct solution to the encoded optimization problem. Finally, we propose quantum-aware modifications of the encoding scheme that avoid this quantum bottleneck and lead to an exponential improvement in the adiabatic performance. This highlights the crucial importance of accounting for quantum effects when designing strategies to encode classical problems onto quantum platforms. 

Serotonin neurons of the dorsal and median raphe nuclei (DR, MR) collectively innervate the entire forebrain and midbrain, modulating diverse physiology and behavior. To gain a fundamental understanding of their molecular heterogeneity, we used plate-based single-cell RNA-sequencing to generate a comprehensive dataset comprising eleven transcriptomically distinct serotonin neuron clusters. Systematic in situ hybridization mapped specific clusters to the principal DR, caudal DR, or MR. These transcriptomic clusters differentially express a rich repertoire of neuropeptides, receptors, ion channels, and transcription factors. We generated novel intersectional viral-genetic tools to access specific subpopulations. Whole-brain axonal projection mapping revealed that DR serotonin neurons co-expressing vesicular glutamate transporter-3 preferentially innervate the cortex, whereas those co-expressing thyrotropin-releasing hormone innervate subcortical regions in particular the hypothalamus. Reconstruction of 50 individual DR serotonin neurons revealed diverse and segregated axonal projection patterns at the single-cell level. Together, these results provide a molecular foundation of the heterogenous serotonin neuronal phenotypes. 

Abstract Purification of C₂H₄from an C₂H₄/C₂H₆mixture is one of the most challenging separation processes, which is achieved mainly through energy‐intensive, cryogenic distillation in industry. Sustainable, non‐distillation methods are highly desired as alternatives. We discovered that the fluorinated bis(pyrazolyl)borate ligand supported copper(I) complex {[(CF₃)₂Bp]Cu}₃has features very desirable in an olefin–paraffin separation material. It binds ethylene exclusively over ethane generating [(CF₃)₂Bp]Cu(C₂H₄). This molecular compound exhibits extremely high and record ideal adsorbed solution theory (IAST) C₂H₄/C₂H₆gas separation selectivity, affording high purity (>99.5 %) ethylene that can be readily desorbed from separation columns. In‐situ PXRD provides a “live” picture of the reversible conversion between [(CF₃)₂Bp]Cu(C₂H₄) and the ethylene‐free sorbent in the solid‐state, driven by the presence or removal of C₂H₄. Molecular structures of trinuclear {[(CF₃)₂Bp]Cu}₃and mononuclear [(CF₃)₂Bp]Cu(C₂H₄) are also presented.