Search

Gaussian process (GP) is a powerful modeling method with applications in machine learning for various engineering and non-engineering fields. Despite numerous benefits of modeling using GPs, the computational complexity associated with GPs demanding immense resources make their practical usage highly challenging. In this article, we develop a quantum version of multi-output Gaussian Process (QGP) by implementing a well-known quantum algorithm called HHL, to perform the Kernel matrix inversion within the Gaussian Process. To reduce the large circuit depth of HHL a circuit optimization technique called Approximate Quantum Compiling (AQC) has been implemented. We further showcase the application of QGP for a real-world problem to estimate line parameters of an electrical grid. Using AQC, up to 13-qubit HHL circuit has been implemented for a 32x32 kernel matrix inversion on IBM Quantum hardware for demonstrating QGP based line parameter estimation experimentally. Finally, we compare its performance against noise-less quantum simulators and classical computation results., Comment: 9 pages, 7 figures

According to the WHO, in 2021, about 32% of pregnant women in sub-Saharan Africa were infected with malaria during pregnancy. Malaria infection during pregnancy can cause various adverse birth outcomes such as low birthweight. Over the past two decades, while some sub-Saharan African areas have experienced a large reduction in malaria prevalence due to improved malaria control and treatments, others have observed little change. Individual-level interventional studies have shown that preventing malaria infection during pregnancy can improve birth outcomes such as birthweight; however, it is still unclear whether natural reductions in malaria prevalence may help improve community-level birth outcomes. We conduct an observational study using 203,141 children's records in 18 sub-Saharan African countries from 2000 to 2018. Using heterogeneity of changes in malaria prevalence, we propose and apply a novel pair-of-pairs design via two-stage bipartite and non-bipartite matching to conduct a difference-in-differences study with a continuous measure of malaria prevalence, namely the Plasmodium falciparum parasite rate among children aged 2 to 10 ($\text{PfPR}_{2-10}$). The proposed novel statistical methodology allows us to apply difference-in-differences without dichotomizing $\text{PfPR}_{2-10}$, which can substantially increase the effective sample size, improve covariate balance, and facilitate the dose-response relationship during analysis. Our outcome analysis finds that among the pairs of clusters we study, the largest reduction in $\text{PfPR}_{2-10}$ over early and late years is estimated to increase the average birthweight by 98.899 grams (95% CI: $[39.002, 158.796]$), which is associated with reduced risks of several adverse birth or life-course outcomes. The proposed novel statistical methodology can be replicated in many other disease areas.

Levels of cancellativity in commutative monoids $M$, determined by stable rank values in $\mathbb{Z}_{> 0} \cup \{\infty\}$ for elements of $M$, are investigated. The behavior of the stable ranks of multiples $ka$, for $k \in \mathbb{Z}_{> 0}$ and $a \in M$, is determined. In the case of a refinement monoid $M$, the possible stable rank values in archimedean components of $M$ are pinned down. Finally, stable rank in monoids built from isomorphism or other equivalence classes of modules over a ring is discussed.

The transition to 100% renewable energy requires new techniques for managing energy networks, such as dividing them into sensible subsets of prosumers called micro-grids. Doing so in an optimal manner is a difficult optimization problem, as it can be abstracted to the Coalition Structure Generation problem in Induced Subgraph Games, a NP-complete problem which requires dividing an undirected, complete, weighted graph into subgraphs in a way that maximizes the sum of their internal weights. Recently, Venkatesh et al. (arXiv:2212.11372) published a Quantum Annealing (QA)-based iterative algorithm called GCS-Q, which they claim to be the best currently existing solver for the problem in terms of runtime complexity. As this algorithm makes the application of QA to the problem seem promising, but is a greedy one, this work proposes several less greedy QA-based approaches and investigates whether any of them can outperform GCS-Q in terms of solution quality. While we find that this is not the case yet on D-Wave hardware, most of them do when using the classical QBSolv software as a solver. Especially an algorithm we call 4-split iterative R-QUBO shows potential here, finding all optima in our dataset while scaling favorably with the problem size in terms of runtime. Thus, it appears to be interesting for future research on quantum approaches to the problem, assuming QA hardware will become more noise-resilient over time., Comment: 7 pages, 4 figures (8 if counting subfigures). To be published in the proceedings of the 2024 IEEE International Conference on Quantum Computing and Engineering (QCE)

Innovative solutions and developments are being inspected to tackle rising electrical power demand to be supplied by clean forms of energy. The integration of renewable energy generations, varying nature loads, importance of active role of distribution system and consumer participation in grid operation has changed the landscape of classical power grids. Implementation of smarter applications to plan, monitor, operate the grid safely are deemed paramount for efficient, secure and reliable functioning of grid. Although sophisticated computations to process gigantic volume of data to produce useful information in a time critical manner is the paradigm of future grid operations, it brings along the burden of computational complexity. Advancements in quantum technologies holds promising solution for dealing with demanding computational complexity of power system related applications. In this article, we lay out clear motivations for seeking quantum solutions for solving computational burden challenges associated with power system applications. Next we present an overview of quantum solutions for various power system related applications available in current literature and suggest future topics for research. We further highlight challenges with existing quantum solutions for exploiting full quantum capabilities. Additionally, this paper serves as a bridge for power engineers to the quantum world by outlining essential quantum computation fundamentals for enabling smoother transition to future of power system computations.

Asteroseismology of dwarf stars cooler than the Sun is very challenging due to the low amplitudes and rapid timescales of oscillations. Here, we present the asteroseismic detection of solar-like oscillations at 4-minute timescales ($\nu_{\mathrm{max}}\sim4300\mu$Hz) in the nearby K-dwarf $\sigma$ Draconis using extreme precision Doppler velocity observations from the Keck Planet Finder and 20-second cadence photometry from NASA's Transiting Exoplanet Survey Satellite. The star is the coolest dwarf star to date with both velocity and luminosity observations of solar-like oscillations, having amplitudes of $5.9\pm0.8\,$cm$\,\text{s}^{-1}$ and $0.8\pm0.2$ ppm, respectively. These measured values are in excellent agreement with established luminosity-velocity amplitude relations for oscillations and provide further evidence that mode amplitudes for stars with $T_{\mathrm{eff}}<\,5500\,$K diminish in scale following a $(L/M)^{1.5}$ relation. By modeling the star's oscillation frequencies from photometric data, we measure an asteroseismic age of $4.5\pm0.9\,\rm{(ran)} \pm 1.2\,\rm{(sys)}$ Gyr. The observations demonstrate the capability of next-generation spectrographs and precise space-based photometry to extend observational asteroseismology to nearby cool dwarfs, which are benchmarks for stellar astrophysics and prime targets for directly imaging planets using future space-based telescopes., Comment: Accepted for publication in The Astrophysical Journal

Electric Vehicles (EVs) are emerging as battery energy storage systems (BESSs) of increasing importance for different power grid services. However, the unique characteristics of EVs makes them more difficult to operate than dedicated BESSs. In this work, we apply a data-driven learning approach to leverage EVs as a BESS to provide capacity-related services to the grid. The approach uses machine learning to predict how to charge and discharge EVs while satisfying their operational constraints. As a paradigm application, we use flexible energy commercialization in the wholesale markets, but the approach can be applied to a broader range of capacity-related grid services. We evaluate the proposed approach numerically and show that when the number of EVs is large, we can obtain comparable objective values to CPLEX and approximate dynamic programming, but with shorter run times. These reduced run times are important because they allow us to (re)optimize frequently to adapt to the time-varying system conditions.

Astronomy is at a turning point in its history and in its relations with the Indigenous peoples who are the generational stewards of land where several of our main observatories are located. The controversy regarding the further development of astronomy facilities on Maunakea is probably the most significant and publicized conflict about the use of such land in the name of science. Thousands have stood in resistance, elders were arrested, and the community is divided. Astronomy's access to one of its most emblematic sites is at risk. This situation challenges our professional practice, the projects we build on Indigenous lands, and our relationships with the people who live within these lands and with society in general. This paper attempts to share the perspective of the authors on the historical events, including the very recent past, through the lens of our understanding and opinions; to provide transparency, with humility, into our process of introspection and transformation; and to share our hopes and ambitions as leaders from Maunakea Observatories for the future of astronomy in Hawai'i, as advocated by the Astro2020 report from the U.S. National Academies of Sciences, Engineering, and Medicine; and to suggest ways for the profession to commit to this long-term vision.

Power grid partitioning is an important requirement for resilient distribution grids. Since electricity production is progressively shifted to the distribution side, dynamic identification of self-reliant grid subsets becomes crucial for operation. This problem can be represented as a modification to the well-known NP-hard Community Detection (CD) problem. We formulate it as a Quadratic Unconstrained Binary Optimization (QUBO) problem suitable for solving using quantum computation{\color{blue}, which is expected to find better-quality partitions faster. The formulation aims to find communities with maximal self-sufficiency and minimal power flowing between them}. To assess quantum optimization for sizeable problems, we apply a hierarchical divisive method that solves sub-problem QUBOs to perform grid bisections. Furthermore, we propose a customization of the Louvain heuristic that includes self-reliance. In the evaluation, we first demonstrate that this problem examines exponential runtime scaling classically. Then, using different IEEE power system test cases, we benchmark the solution quality for multiple approaches: D-Wave's hybrid quantum-classical solvers, classical heuristics, and a branch-and-bound solver. As a result, we observe that the hybrid solvers provide very promising results, both with and without the divisive algorithm, regarding solution quality achieved within a given time frame. Directly utilizing D-Wave's Quantum Annealing (QA) hardware shows inferior partitioning.

The W. M. Keck Observatory is welcoming a new era where data reduction and archiving are tightly integrated into our observing model, under the auspices of the Observatory's Data Services Initiative (DSI) project. While previously the Keck Observatory Archive (KOA) archived minimally processed, raw science data the day after observing, Keck is transitioning to a model in which it archives both raw frames and reduced data in near real-time. These data will be made available to observers and collaborators immediately upon ingestion through a dedicated new interface that will support collaboration and sharing among teams, as well as stream data directly to personal computers without access to WMKO's internal networks. Both the raw and science-ready data products will be made publicly available upon the expiration of data protections., Comment: 10 pages, 5 figures, SPIE Astronomical Telescopes + Instrumentation 2022

We give a number of constructions where inverse limits seriously degrade properties of regular rings, such as unit-regularity, diagonalisation of matrices, and finite stable rank. This raises the possibility of using inverse limits to answer the long standing Separativity Problem (in the negative)., Comment: Accepted for publication in Journal of Algebra. Minor edits + new section, developing a surjective inverse limit $Q = \varprojlim Q_i$ of regular rings such that $V(Q)$ is not isomorphic to $\varprojlim V(Q_i)$

In this project summary paper, we summarize the key results and use-cases explored in the German Federal Ministry of Education and Research (BMBF) funded project "Q-GRID" which aims to assess potential quantum utility optimization applications in the electrical grid. The project focuses on two layers of optimization problems relevant to decentralized energy generation and transmission as well as novel energy transportation/exchange methods such as Peer-2-Peer energy trading and microgrid formation. For select energy grid optimization problems, we demonstrate exponential classical optimizer runtime scaling even for small problem instances, and present initial findings that variational quantum algorithms such as QAOA and hybrid quantum annealing solvers may provide more favourable runtime scaling to obtain similar solution quality. These initial results suggest that quantum computing may be a key enabling technology in the future energy transition insofar that they may be able to solve business problems which are already challenging at small problem instance sizes., Comment: 12 pages. arXiv admin note: text overlap with arXiv:2309.05502

We analyze the \ovi\ content and kinematics for 126 {\hi}-selected absorbers at $0.14 \lesssim z \lesssim 0.73$ for which the metallicities of their cool photoionized phase have been determined. We separate the absorbers into 100 strong {\lya} forest systems (SLFSs with $15 \la$\,{\colden}\,$< 16.2$) and 26 partial Lyman Limit systems (pLLSs with $16.2\le$\,{\colden}\,$\le 17.2$). The sample is drawn from the COS CGM Compendium (CCC) and has \ovi\ coverage in $\sn \geq 8$ {\it HST}/COS G130M/G160M QSO spectra, yielding a $2\sigma$ completeness level of {\coldenovi}$\,\geq 13.6$. The \ovi\ detection rates differ substantially between low-metallicity (LM; {\xh} $\leq -1.4$) and high-metallicity (HM; {\xh} $> -1.4$) SLFSs, with 20\% and 60\% detection rates, respectively. The \ovi\ detection frequency for the HM and LM pLLSs is, however, similar at $\sim$60\%. The SLFSs and pLLSs without detected \ovi\ are consistent with the absorbing gas being in a single phase, while those with \ovi\ trace multiphase gas. We show that the \ovi\ velocity widths and column densities have different distributions in LM and HM gas. We find a strong correlation between \ovi\ column density and metallicity. The strongest (\coldenovi$\,\ga 14$) and broadest {\ovi} absorbers are nearly always associated with HM absorbers, while weaker \ovi\ absorbers are found in both LM and HM absorbers. From comparisons with galaxy-selected and blind \ovi\ surveys, we conclude absorbers with \coldenovi$\,\ga 14$ most likely arise in the circumgalactic medium (CGM) of star-forming galaxies. Absorbers with weak \ovi\ likely trace the extended CGM or intergalactic medium (IGM), while those without \ovi\ likely originate in the IGM., Comment: Accepted for publication in ApJ

In this article, the issue of choice cuts made to a rectangular region are considered and explored. Results show that this problem is not trivial. Outcomes for teaching and learning are considered., Comment: 7 pages, 5 figures

Recent advances in quantum computers are demonstrating the ability to solve problems at a scale beyond brute force classical simulation. As such, a widespread interest in quantum algorithms has developed in many areas, with optimization being one of the most pronounced domains. Across computer science and physics, there are a number of different approaches for major classes of optimization problems, such as combinatorial optimization, convex optimization, non-convex optimization, and stochastic extensions. This work draws on multiple approaches to study quantum optimization. Provably exact versus heuristic settings are first explained using computational complexity theory - highlighting where quantum advantage is possible in each context. Then, the core building blocks for quantum optimization algorithms are outlined to subsequently define prominent problem classes and identify key open questions that, if answered, will advance the field. The effects of scaling relevant problems on noisy quantum devices are also outlined in detail, alongside meaningful benchmarking problems. We underscore the importance of benchmarking by proposing clear metrics to conduct appropriate comparisons with classical optimization techniques. Lastly, we highlight two domains - finance and sustainability - as rich sources of optimization problems that could be used to benchmark, and eventually validate, the potential real-world impact of quantum optimization., Comment: Updated title to match journal version

Busy educators are often faced with a dilemma--staying up to date with evidence-based practices and initiatives that support their professional growth while combating a constant barrage of superficial ideas from other contexts. Continuous improvement approaches to change offer methods for testing new ideas and adapting them to local contexts. If seen as add-ons to educators' roles, however, these approaches are unlikely to yield deep engagement and sustained improvement. This practice brief explains the CORE Districts' five-driver model, a modified approach that deepened educators' ownership of the work and positioned schools to sustain improvement over time.

The astronomical community is grappling with the increasing volume and complexity of data produced by modern telescopes, due to difficulties in reducing, accessing, analyzing, and combining archives of data. To address this challenge, we propose the establishment of a coordinating body, an "entity," with the specific mission of enhancing the interoperability, archiving, distribution, and production of both astronomical data and software. This report is the culmination of a workshop held in February 2023 on the Future of Astronomical Data Infrastructure. Attended by 70 scientists and software professionals from ground-based and space-based missions and archives spanning the entire spectrum of astronomical research, the group deliberated on the prevailing state of software and data infrastructure in astronomy, identified pressing issues, and explored potential solutions. In this report, we describe the ecosystem of astronomical data, its existing flaws, and the many gaps, duplication, inconsistencies, barriers to access, drags on productivity, missed opportunities, and risks to the long-term integrity of essential data sets. We also highlight the successes and failures in a set of deep dives into several different illustrative components of the ecosystem, included as an appendix., Comment: 59 pages; please send comments and/or questions to foadi@googlegroups.com

Demand Side Response (DSR) is a strategy that enables consumers to actively participate in managing electricity demand. It aims to alleviate strain on the grid during high demand and promote a more balanced and efficient use of (renewable) electricity resources. We implement DSR through discount scheduling, which involves offering discrete price incentives to consumers to adjust their electricity consumption patterns to times when their local energy mix consists of more renewable energy. Since we tailor the discounts to individual customers' consumption, the Discount Scheduling Problem (DSP) becomes a large combinatorial optimization task. Consequently, we adopt a hybrid quantum computing approach, using D-Wave's Leap Hybrid Cloud. We benchmark Leap against Gurobi, a classical Mixed Integer optimizer in terms of solution quality at fixed runtime and fairness in terms of discount allocation. Furthermore, we propose a large-scale decomposition algorithm/heuristic for the DSP, applied with either quantum or classical computers running the subroutines, which significantly reduces the problem size while maintaining solution quality. Using synthetic data generated from real-world data, we observe that the classical decomposition method obtains the best overall \newp{solution quality for problem sizes up to 3200 consumers, however, the hybrid quantum approach provides more evenly distributed discounts across consumers.

Continuous improvement has a prominent place in California's approach to educational accountability. But while there are proof points that show the potential of continuous improvement, currently there is not evidence that continuous improvement efforts are consistently leading to sustainable improvement in student outcomes and system functioning. This report analyzes the experiences of two organizations serving as the hubs of improvement networks, both of which led networks seeking to increase the proportion of students on track for postsecondary success during the 2020-21 school year. The researchers analyzed their work to learn lessons about how to improve the consistency of success of initiatives using continuous improvement. They analyzed data to examine the improvement work of each school or district participating in a learning network. For analysis, they used Coburn's (2003) four dimensions of reform at scale--depth, spread, ownership, and sustainability--as indicators of traction. [For the summary, "Supporting Improvement in Networks: Key Takeaways for Districts, County Offices, and External Support Providers," see ED624979.]

During the 2020-2021 school year, the Policy Analysis for California Education (PACE) examined two hubs leading continuous improvement networks focused on increasing the rate at which students are on track to graduate high school positioned for postsecondary success. Both hubs offered: (1) coaching, with expectation of monthly meetings; (2) professional development from hub staff and external content experts; (3) frameworks, tools, and common data to promote reform efforts; and (4) access to a network of sites doing similar work. This is a companion document to the report "Generating Traction with Continuous Improvement: Lessons from Two Learning Networks" (ED624973). It summarizes the main findings about the ways hubs of learning networks--external support providers, county offices of education, or districts working with their schools--can help an improvement project gain traction toward improving student outcomes at scale.