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  1. Realizing quantum speedup for practically relevant, computationally hard problems is a central challenge in quantum information science. Using Rydberg atom arrays with up to 289 qubits in two spatial dimensions, we experimentally investigate quantum algorithms for solving the Maximum Independent Set problem. We use a hardware-efficient encoding associated with Rydberg blockade, realize closed-loop optimization to test several variational algorithms, and subsequently apply them to systematically explore a class of graphs with programmable connectivity. We find the problem hardness is controlled by the solution degeneracy and number of local minima, and experimentally benchmark the quantum algorithm’s performance against classical simulated annealing.more »On the hardest graphs, we observe a superlinear quantum speedup in finding exact solutions in the deep circuit regime and analyze its origins.« less
    Free, publicly-accessible full text available May 5, 2023
  2. Bayesian hybrid models fuse physics-based insights with machine learning constructs to correct for systematic bias. In this paper, we compare Bayesian hybrid models against physics-based glass-box and Gaussian process black-box surrogate models. We consider ballistic firing as an illustrative case study for a Bayesian decision-making workflow. First, Bayesian calibration is performed to estimate model parameters. We then use the posterior distribution from Bayesian analysis to compute optimal firing conditions to hit a target via a single-stage stochastic program. The case study demonstrates the ability of Bayesian hybrid models to overcome systematic bias from missing physics with fewer data than themore »pure machine learning approach. Ultimately, we argue Bayesian hybrid models are an emerging paradigm for data-informed decision-making under parametric and epistemic uncertainty.« less
    Free, publicly-accessible full text available July 27, 2022
  3. Stochastic gradient Langevin dynamics (SGLD) and stochastic gradient Hamiltonian Monte Carlo (SGHMC) are two popular Markov Chain Monte Carlo (MCMC) algorithms for Bayesian inference that can scale to large datasets, allowing to sample from the posterior distribution of the parameters of a statistical model given the input data and the prior distribution over the model parameters. However, these algorithms do not apply to the decentralized learning setting, when a network of agents are working collaboratively to learn the parameters of a statistical model without sharing their individual data due to privacy reasons or communication constraints. We study two algorithms: Decentralizedmore »SGLD (DE-SGLD) and Decentralized SGHMC (DE-SGHMC) which are adaptations of SGLD and SGHMC methods that allow scaleable Bayesian inference in the decentralized setting for large datasets. We show that when the posterior distribution is strongly log-concave and smooth, the iterates of these algorithms converge linearly to a neighborhood of the target distribution in the 2-Wasserstein distance if their parameters are selected appropriately. We illustrate the efficiency of our algorithms on decentralized Bayesian linear regression and Bayesian logistic regression problems« less
  4. Opioid overdose rescue is very time-sensitive. Hence, drone-delivered naloxone has the potential to be a transformative innovation due to its easily deployable and flexible nature. We formulate a Markov Decision Process (MDP) model to dispatch the appropriate drone after an overdose request arrives and to relocate the drone to its next waiting location after having completed its current task. Since the underlying optimization problem is subject to the curse of dimensionality, we solve it using ad-hoc state aggregation and evaluate it through a simulation with higher granularity. Our simulation-based comparative study is based on emergency medical service data from themore »state of Indiana. We compare the optimal policy resulting from the scaled-down MDP model with a myopic policy as the baseline. We consider the impact of drone type and service area type on outcomes, which offers insights into the performance of the MDP suboptimal policy under various settings.« less
  5. ABSTRACT In this work, we combine spectroscopic information from the SkyMapper survey for Extremely Metal-Poor stars and astrometry from Gaia DR2 to investigate the kinematics of a sample of 475 stars with a metallicity range of $-6.5 \le \rm [Fe/H] \le -2.05$ dex. Exploiting the action map, we identify 16 and 40 stars dynamically consistent with the Gaia Sausage and Gaia Sequoia accretion events, respectively. The most metal poor of these candidates have metallicities of $\rm [Fe/H]=-3.31\, \mathrm{ and }\, -3.74$, respectively, helping to define the low-metallicity tail of the progenitors involved in the accretion events. We also find, consistentmore »with other studies, that ∼21 per cent of the sample have orbits that remain confined to within 3 kpc of the Galactic plane, that is, |Zmax| ≤ 3 kpc. Of particular interest is a subsample (∼11 per cent of the total) of low |Zmax| stars with low eccentricities and prograde motions. The lowest metallicity of these stars has [Fe/H] = –4.30 and the subsample is best interpreted as the very low-metallicity tail of the metal-weak thick disc population. The low |Zmax|, low eccentricity stars with retrograde orbits are likely accreted, while the low |Zmax|, high eccentricity pro- and retrograde stars are plausibly associated with the Gaia Sausage system. We find that a small fraction of our sample (∼4 per cent of the total) is likely escaping from the Galaxy, and postulate that these stars have gained energy from gravitational interactions that occur when infalling dwarf galaxies are tidally disrupted.« less