Lifetimes of ultralong-range Rydberg molecules in vibrational ground and excited state

Since their first experimental observation, ultralong-range Rydberg molecules consisting of a highly excited Rydberg atom and a ground state atom have attracted the interest in the field of ultracold chemistry. Especially the intriguing properties like size, polarizability and type of binding they inherit from the Rydberg atom are of interest. An open question in the field is the reduced lifetime of the molecules compared to the corresponding atomic Rydberg states. In this letter we present an experimental study on the lifetimes of the ^3\Sigma (5s-35s) molecule in its vibrational ground state and in an excited state. We show that the lifetimes depends on the density of ground state atoms and that this can be described in the frame of a classical scattering between the molecules and ground state atoms. We also find that the excited molecular state has an even more reduced lifetime compared to the ground state which can be attributed to an inward penetration of the bound atomic pair due to imperfect quantum reflection that takes place in the special shape of the molecular potential

The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism

We present scalable hybrid-parallel algorithms for training large-scale 3D convolutional neural networks. Deep learning-based emerging scientific workflows often require model training with large, high-dimensional samples, which can make training much more costly and even infeasible due to excessive memory usage. We solve these challenges by extensively applying hybrid parallelism throughout the end-to-end training pipeline, including both computations and I/O. Our hybrid-parallel algorithm extends the standard data parallelism with spatial parallelism, which partitions a single sample in the spatial domain, realizing strong scaling beyond the mini-batch dimension with a larger aggregated memory capacity. We evaluate our proposed training algorithms with two challenging 3D CNNs, CosmoFlow and 3D U-Net. Our comprehensive performance studies show that good weak and strong scaling can be achieved for both networks using up 2K GPUs. More importantly, we enable training of CosmoFlow with much larger samples than previously possible, realizing an order-of-magnitude improvement in prediction accuracy.Comment: 12 pages, 10 figure

Measurement of Longitudinal Spin Asymmetries for Weak Boson Production in Polarized Proton-Proton Collisions at RHIC

We report measurements of single- and double-spin asymmetries for W[superscript ±] and Z/γ[superscript ∗] boson production in longitudinally polarized p + p collisions at √s = 510  GeV by the STAR experiment at RHIC. The asymmetries for W[superscript ±] were measured as a function of the decay lepton pseudorapidity, which provides a theoretically clean probe of the proton’s polarized quark distributions at the scale of the W mass. The results are compared to theoretical predictions, constrained by polarized deep inelastic scattering measurements, and show a preference for a sizable, positive up antiquark polarization in the range 0.05 < x < 0.2.Brookhaven National LaboratoryLawrence Berkeley National LaboratoryUnited States. Dept. of Energy. Office of Nuclear PhysicsUnited States. Dept. of Energy. Office of High Energy PhysicsNational Science Foundation (U.S.

Q[subscript weak]: First Direct Measurement of the Proton’s Weak Charge

The Q[subscript weak] experiment, which took data at Jefferson Lab in the period 2010 - 2012, will precisely determine the weak charge of the proton by measuring the parity-violating asymmetry in elastic e-p scattering at 1.1 GeV using a longitudinally polarized electron beam and a liquid hydrogen target at a low momentum transfer of Q² = 0.025 (GeV/c)². The weak charge of the proton is predicted by the Standard Model and any significant deviation would indicate physics beyond the Standard Model. The technical challenges and experimental apparatus for measuring the weak charge of the proton will be discussed, as well as the method of extracting the weak charge of the proton. The results from a small subset of the data, that has been published, will also be presented. Furthermore an update will be given of the current status of the data analysis

Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models

Single neuron models are fundamental for computational modeling of the brain's neuronal networks, and understanding how ion channel dynamics mediate neural function. A challenge in defining such models is determining biophysically realistic channel distributions. Here, we present an efficient, highly parallel evolutionary algorithm for developing such models, named NeuroGPU-EA. NeuroGPU-EA uses CPUs and GPUs concurrently to simulate and evaluate neuron membrane potentials with respect to multiple stimuli. We demonstrate a logarithmic cost for scaling the stimuli used in the fitting procedure. NeuroGPU-EA outperforms the typically used CPU based evolutionary algorithm by a factor of 10 on a series of scaling benchmarks. We report observed performance bottlenecks and propose mitigation strategies. Finally, we also discuss the potential of this method for efficient simulation and evaluation of electrophysiological waveforms

[J over ψ] polarization in p+p collisions at √s = 200 TeV in STAR

We report on a polarization measurement of inclusive [J over ψ] mesons in the di-electron decay channel at mid-rapidity at 2<p[subscript T]<6 GeV/c in p+p collisions at √s = 200 GeV. Data were taken with the STAR detector at RHIC. The [J over ψ] polarization measurement should help to distinguish between different models of the [J over ψ] production mechanism since they predict different p[subscript T] dependences of the [J over ψ] polarization. In this analysis, [J over ψ] polarization is studied in the helicity frame. The polarization parameter λ[subscript θ] measured at RHIC becomes smaller towards high p[subscript T], indicating more longitudinal [J over ψ] polarization as p[subscript T] increases. The result is compared with predictions of presently available models.United States. Dept. of Energy. Office of ScienceNational Science Foundation (U.S.

Beam Energy Dependence of Moments of the Net-Charge Multiplicity Distributions in Au + Au Collisions at RHIC

We report the first measurements of the moments—mean (M), variance (σ[superscript 2]), skewness (S), and kurtosis (κ)—of the net-charge multiplicity distributions at midrapidity in Au + Au collisions at seven energies, ranging from √s[subscript NN] = 7.7 to 200 GeV, as a part of the Beam Energy Scan program at RHIC. The moments are related to the thermodynamic susceptibilities of net charge, and are sensitive to the location of the QCD critical point. We compare the products of the moments, σ[superscript 2]/M, Sσ, and κσ[superscript 2], with the expectations from Poisson and negative binomial distributions (NBDs). The Sσ values deviate from the Poisson baseline and are close to the NBD baseline, while the κσ[superscript 2] values tend to lie between the two. Within the present uncertainties, our data do not show nonmonotonic behavior as a function of collision energy. These measurements provide a valuable tool to extract the freeze-out parameters in heavy-ion collisions by comparing with theoretical models.Brookhaven National LaboratoryLawrence Berkeley National LaboratoryUnited States. Dept. of Energy. Office of Nuclear PhysicsUnited States. Dept. of Energy. Office of High Energy PhysicsNational Science Foundation (U.S.

Quantum-parallel vectorized data encodings and computations on trapped-ion and transmon QPUs

Abstract Compact data representations in quantum systems are crucial for the development of quantum algorithms for data analysis. In this study, we present two innovative data encoding techniques, known as QCrank and QBArt, which exhibit significant quantum parallelism via uniformly controlled rotation gates. The QCrank method encodes a series of real-valued data as rotations on data qubits, resulting in increased storage capacity. On the other hand, QBArt directly incorporates a binary representation of the data within the computational basis, requiring fewer quantum measurements and enabling well-established arithmetic operations on binary data. We showcase various applications of the proposed encoding methods for various data types. Notably, we demonstrate quantum algorithms for tasks such as DNA pattern matching, Hamming weight computation, complex value conjugation, and the retrieval of a binary image with 384 pixels, all executed on the Quantinuum trapped-ion QPU. Furthermore, we employ several cloud-accessible QPUs, including those from IBMQ and IonQ, to conduct supplementary benchmarking experiments