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We propose a semiparametric Bayesian methodology for estimating the average treatment effect (ATE) within the potential outcomes framework using observational data with high-dimensional nuisance parameters. Our method introduces a Bayesian debiasing procedure that corrects for bias arising from nuisance estimation and employs a targeted modeling strategy based on summary statistics rather than the full data. These summary statistics are identified in a debiased manner, enabling the estimation of nuisance bias via weighted observables and facilitating hierarchical learning of the ATE. By combining debiasing with sample splitting, our approach separates nuisance estimation from inference on the target parameter, reducing sensitivity to nuisance model specification. We establish that, under mild conditions, the marginal posterior for the ATE satisfies a Bernstein-von Mises theorem when both nuisance models are correctly specified and remains consistent and robust when only one is correct, achieving Bayesian double robustness. This ensures asymptotic efficiency and frequentist validity. Extensive simulations confirm the theoretical results, demonstrating accurate point estimation and credible intervals with nominal coverage, even in high-dimensional settings. The proposed framework can also be extended to other causal estimands, and its key principles offer a general foundation for advancing Bayesian semiparametric inference more broadly. 

We report spectroscopic and time-resolved experimental observations to characterize the $\left[\mathrm{Xe}\right]4f^{13}\left({}^{2}F_{5/2}^o\right)5d6s\left({}^{1}D\right){}^1{[5/2]}_{5/2}^o$state in ${}^{172}\mathrm{Yb}^{+}$ ions. We access this state from the metastable $4f^{14}5d\left({}^{2}D_{3/2,5/2}\right)$manifold and observe an unexpectedly long lifetime of $\tau =37.9\left(9\right)\mu \mathrm{s}$ that allows visible Rabi oscillations and resolved-sideband spectroscopy. Using a combination of coherent population dynamics, high-fidelity detection and heralded state preparation, and optical pumping methods, we measure the branching ratios to the ${}^{2}D_{3/2}, {}^{2}D_{5/2}$, and ${}^{2}S_{1/2}$states to be $0.359\left(2\right)$, 0.639(2), and $0.0023\left(16\right)$, respectively. The branching ratio to the $4f^{13}6s^2\left({{}^{2}F}_{7/2}\right)$is compatible with zero within our experimental resolution. We also report measurements of Landé $g$-factor of the ${}^1{[5/2]}_{5/2}^o$state. Further, the branching ratio of the ${}^{2}D_{5/2}$to ${}^{2}S_{1/2}$decay in ${}^{172}\mathrm{Yb}^{+}$ was measured to be 0.188(3), improving its relative uncertainty by an order of magnitude. Our measurements provide experimental benchmarks for better understanding the atomic structure of ${\mathrm{Yb}}^{+}$ ions, which still lacks accurate numerical descriptions, and the use of high-lying excited states for partial detection and qubit manipulation in the architecture. 

Quantum Annealing (QA)-accelerated MIMO detection is an emerging research approach in the context of NextG wireless networks. The opportunity is to enable large MIMO systems and thus improve wireless performance. The approach aims to leverage QA to expedite the computation required for theoretically optimal but computationally-demanding Maximum Likelihood detection to overcome the limitations of the currently deployed linear detectors. This paper presents X-ResQ, a QA-based MIMO detector system featuring flexible parallelism that is uniquely enabled by quantum Reverse Annealing (RA). Unlike prior designs, X-ResQ has many desirable parallel QA system properties and has effectively improved detection performance as more qubits are assigned. In our evaluations on a state-of-the-art quantum annealer, fully parallel X-ResQ achieves near-optimal throughput for 4 ×6 MIMO with 16-QAM using approx. 240 qubits achieving 2.5–5× gains compared against other classical and quantum detectors. We also implement and evaluate X-ResQ in the non-quantum digital setting for more comprehensive evaluations. This classical X-ResQ showcases the potential to realize ultra-large 1024 ×1024 MIMO, significantly outperforming other MIMO detectors, including the state-of-the-art RA detector classically implemented in the same way. 

This study introduces a carbonate-surface-mimicking microfluidic platform and demonstrates its pore-scale oil displacement behavior using seawater–concrete–CO₂flooding compared with conventional fluids. 

Abstract Particle collisions at accelerators like the Large Hadron Collider (LHC), recorded by experiments such as ATLAS and CMS, enable precise standard model measurements and searches for new phenomena. Simulating these collisions significantly influences experiment design and analysis but incurs immense computational costs, projected at millions of CPU-years annually during the high luminosity LHC (HL-LHC) phase. Currently, simulating a single event with Geant4 consumes around 1000 CPU seconds, with calorimeter simulations especially demanding. To address this, we propose a conditioned quantum-assisted generative model, integrating a conditioned variational autoencoder (VAE) and a conditioned restricted Boltzmann machine (RBM). Our RBM architecture is tailored for D-Wave’s Pegasus-structured advantage quantum annealer for sampling, leveraging the flux bias for conditioning. This approach combines classical RBMs as universal approximators for discrete distributions with quantum annealing’s speed and scalability. We also introduce an adaptive method for efficiently estimating effective inverse temperature, and validate our framework on Dataset 2 of CaloChallenge. 

The interplay between coherence and system-environment interactions is at the basis of a wide range of phenomena, from quantum information processing to charge and energy transfer in molecular systems, biomolecules, and photochemical materials. In this work, we use a Frenkel exciton model with long-range interacting qubits coupled to a damped collective bosonic mode to investigate vibrationally assisted transfer processes in donor-acceptor systems featuring internal substructures analogous to light-harvesting complexes. We find that certain delocalized excitonic states maximize the transfer rate and that the entanglement is preserved during the dissipative transfer over a wide range of parameters. We investigate the reduction in transfer caused by static disorder, white noise, and finite temperature and study how transfer efficiency scales as a function of the number of dimerized monomers and the component number of each monomer, finding which excitonic states lead to optimal transfer. Finally, we provide a realistic experimental setting to realize this model in analog trapped-ion quantum simulators. Analog quantum simulation of systems comprising many and increasingly complex monomers could offer valuable insights into the design of light-harvesting materials, particularly in the nonperturbative intermediate parameter regime examined in this study, where classical simulation methods are resource intensive. 

We consider the problem of constructing confidence intervals for the locations of change points in a high-dimensional mean shift model. We develop a locally refitted least squares estimator and obtain component-wise and simultaneous rates of estimation of change points. The simultaneous rate is the sharpest available by at least a factor of log p, while the component-wise one is optimal. These results enable existence of limiting distributions for the locations of the change points. Subsequently, component-wise distributions are characterized under both vanishing and non-vanishing jump size regimes, while joint distributions of change point estimates are characterized under the latter regime, which also yields asymptotic independence of these estimates. We provide the relationship between these distributions, which allows construction of regime adaptive confidence intervals. All results are established under a high dimensional scaling, in the presence of diverging number of change points. They are illustrated on synthetic data and on sensor measurements from smartphones for activity recognition.