Andrea Delgado

Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with the potential for achieving a so-called quantum advantage, namely a significant (in some cases exponential) speed-up of numerical simulations. The rapid development of hardware devices with various realizations of qubits enables the execution of small scale but representative applications on quantum computers. In particular, the high-energy physics community plays a pivotal role in accessing the power of quantum computing, since the field is a driving source for challenging computational problems. This concerns, on the theoretical side, the exploration of models which are very hard or even impossible to address with classical techniques and, on the experimental side, the enormous data challenge of newly emerging experiments, such as the upgrade of the Large Hadron Collider. In this roadmap paper, led by CERN, DESY and IBM, we provide the status of high-energy physics quantum computations and give examples for theoretical and experimental target benchmark applications, which can be addressed in the near future. Having the IBM 100 x 100 challenge in mind, where possible, we also provide resource estimates for the examples given using error mitigated quantum computing.

A joint determination of the reactor antineutrino spectra resulting from the fission of $^{235}$U and $^{239}$Pu has been carried out by the Daya Bay and PROSPECT collaborations. This Letter reports the level of consistency of $^{235}$U spectrum measurements from the two experiments and presents new results from a joint analysis of both data sets. The measurements are found to be consistent. The combined analysis reduces the degeneracy between the dominant $^{235}$U and $^{239}$Pu isotopes and improves the uncertainty of the $^{235}$U spectral shape to about 3\%. The ${}^{235}$U and $^{239}$Pu antineutrino energy spectra are unfolded from the jointly deconvolved reactor spectra using the Wiener-SVD unfolding method, providing a data-based reference for other reactor antineutrino experiments and other applications. This is the first measurement of the $^{235}$U and $^{239}$Pu spectra based on the combination of experiments at low- and highly enriched uranium reactors.

The PROSPECT and STEREO collaborations present a combined measurement of the pure ^{235}U antineutrino spectrum, without site specific corrections or detector-dependent effects. The spectral measurements of the two highest precision experiments at research reactors are found to be compatible with χ^{2}/ndf=24.1/21, allowing a joint unfolding of the prompt energy measurements into antineutrino energy. This ν[over ¯]_{e} energy spectrum is provided to the community, and an excess of events relative to the Huber model is found in the 5-6 MeV region. When a Gaussian bump is fitted to the excess, the data-model χ^{2} value is improved, corresponding to a 2.4σ significance.

Continuous-wave tunable laser operation of direct diode-pumped Cr4+:Mg2 SiO4 (Cr:forsterite) and Cr4+:Ca2GeO4 (cunyite) crystal were demonstrated. Diode-pumped Cr:forsterite was tunable over the 1236–1300-nm spectral range. The maximum output of 10 mW was measured at 1260 nm for 640 mW of pump power absorbed by the crystal. Diode-pumped laser operation of cunyite was also demonstrated over the 1390–1475-nm range. Free-running 20-mW output was centered at 1410 nm.

Sclerotinia sclerotiorum is a phytopathogenic fungus that causes wilting and white rot in several species such as strawberry. The overuse of agrochemicals has caused environmental pollution and plant resistance to phytopathogens. Inoculation of crops with beneficial microorganisms such as arbuscular mycorrhizae (AM), plant-growth-promoting rhizobacteria (PGPR), and their metabolites is considered as an alternative to agrochemicals. B.halotolerans IcBac2.1 (BM) and Bacillus TrujBac2.32 (B), native from Peruvian soils, produce antifungal compounds and are plant-growth-promoting rhizobacteria (PGPR). B. halotolerans IcBac2.1 and Bacillus TrujBac2 with or without G. intraradices mycorrhizal fungi (M) are capable of controlling S. sclerotiorum disease in strawberries. Inoculation of mycorrhiza alone decreases disease incidence as well. Treatments with chitosan (Ch), which is used to elicit plant defense responses against fungal pathogens, were used for comparison, as well as non-inoculated plants (C). Co-inoculation of mycorrhiza and bacteria increases plant shoot and root biomass. Our results show that the inoculation of arbuscular mycorrhiza and antifungal Bacillus are good biocontrols of S. sclerotiorum in strawberry.

In preparation for the 2023 NSAC Long Range Plan (LRP), members of the Nuclear Science community gathered to discuss the current state of, and plans for further leveraging opportunities in, QIST in NP research at the Quantum Information Science for U.S. Nuclear Physics Long Range Planning workshop, held in Santa Fe, New Mexico on January 31 - February 1, 2023. The workshop included 45 in-person participants and 53 remote attendees. The outcome of the workshop identified strategic plans and requirements for the next 5-10 years to advance quantum sensing and quantum simulations within NP, and to develop a diverse quantum-ready workforce. The plans include resolutions endorsed by the participants to address the compelling scientific opportunities at the intersections of NP and QIST. These endorsements are aligned with similar affirmations by the LRP Computational Nuclear Physics and AI/ML Workshop, the Nuclear Structure, Reactions, and Astrophysics LRP Town Hall, and the Fundamental Symmetries, Neutrons, and Neutrinos LRP Town Hall communities.

This Letter reports one of the most precise measurements to date of the antineutrino spectrum from a purely ^{235}U-fueled reactor, made with the final dataset from the PROSPECT-I detector at the High Flux Isotope Reactor. By extracting information from previously unused detector segments, this analysis effectively doubles the statistics of the previous PROSPECT measurement. The reconstructed energy spectrum is unfolded into antineutrino energy and compared with both the Huber-Mueller model and a spectrum from a commercial reactor burning multiple fuel isotopes. A local excess over the model is observed in the 5-7 MeV energy region. Comparison of the PROSPECT results with those from commercial reactors provides new constraints on the origin of this excess, disfavoring at 2.0 and 3.7 standard deviations the hypotheses that antineutrinos from ^{235}U are solely responsible and noncontributors to the excess observed at commercial reactors, respectively.

Some of the biggest achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and operating large-scale experiments like the Large Hadron Collider or the Tevatron. In these facilities, the ultimate theory to describe matter at the most fundamental level is constantly probed and verified. These experiments often produce large amounts of data that require storing, processing, and analysis techniques that often push the limits of traditional information processing schemes. Thus, the High-Energy Physics (HEP) field has benefited from advancements in information processing and the development of algorithms and tools for large datasets. More recently, quantum computing applications have been investigated in an effort to understand how the community can benefit from the advantages of quantum information science. In this manuscript, we provide an overview of the state-of-the-art applications of quantum computing to data analysis in HEP, discuss the challenges and opportunities in integrating these novel analysis techniques into a day-to-day analysis workflow, and whether there is potential for a quantum advantage.

Abstract Graphitic nanofillers reinforced epoxy coatings have been manufactured using UV‐photopolymerized resin. These materials present effective low‐power resistive heating, which can be potentially useful for deicing and anti‐icing devices. During the optimization of UV‐3D manufacturing printing process, it was confirmed that the coating thickness strongly depends on the nature and content of graphitic nanofillers. Carbon nanotubes (CNTs) strongly inhibit the photopolymerization because of the UV light dispersion, which competes with the light absorption of the photoinitiator, decreasing the coating thickness. Thermo‐mechanical behavior of the doped coatings has been analyzed together with their efficiency as de‐icing materials. The highest self‐heating achieved by Joule's effect was measured for the coating doped with the lower studied content of CNTs, close to electrical percolation. This is explained by its high‐electrical conductivity and the higher contribution of tunneling effect regard to the electrical conduction by direct contact.

Quantum computing holds the promise of substantially speeding up computationally expensive tasks, such as solving optimization problems over a large number of elements. In high-energy collider physics, quantum-assisted algorithms might accelerate the clustering of particles into jets. In this study, we benchmark quantum annealing strategies for jet clustering based on optimizing a quantity called "thrust" in electron-positron collision events. We find that quantum annealing yields similar performance to exact classical approaches and classical heuristics, but only after tuning the annealing parameters. Without tuning, comparable performance can be obtained through a hybrid quantum/classical approach.

Unsupervised training of generative models is a machine learning task that has many applications in scientific computing. In this work we evaluate the efficacy of using quantum circuit-based generative models to generate synthetic data of high energy physics processes. We use non-adversarial, gradient-based training of quantum circuit Born machines to generate joint distributions over 2 and 3 variables.

The recent demonstration of the existence of an intimate relationship between the chiral structure of some materials and the spin polarization of electrons transmitted through them, what has been called the chirality-induced spin selectivity (CISS) effect, is sparking interest in many related phenomena. One of the most notorious is the possibility of using magnetic materials to apply enantioselective interactions on chiral molecules and chemical reactions involving them. In this work, x-ray photoelectron spectroscopy has been used to characterize the adsorption and growth kinetics of enantiopure organic molecules on magnetic (Co) and non-magnetic (Cu) substrates. While on these latter, no significant enantiosensitive effects are found, on spin-polarized, in-plane magnetized Co surfaces, the two enantiomers have been found to deposit differently. The observed effects have been interpreted as the result of one of the enantiomers being adsorbed in a transient, weakly bound physisorbed-like state with higher mobility due to limited, spin-selective charge transfer between it and the substrate. The study of these phenomena can provide insight into the fundamental mechanisms responsible for the CISS effect.

Pattern recognition algorithms are commonly employed to simplify the challenging and necessary step of track reconstruction in sub-atomic physics experiments. Aiding in the discrimination of relevant interactions, pattern recognition seeks to accelerate track reconstruction by isolating signals of interest. In high collision rate experiments, such algorithms can be particularly crucial for determining whether to retain or discard information from a given interaction even before the data is transferred to tape. As data rates, detector resolution, noise, and inefficiencies increase, pattern recognition becomes more computationally challenging, motivating the development of higher efficiency algorithms and techniques. Quantum associative memory is an approach that seeks to exploits quantum mechanical phenomena to gain advantage in learning capacity, or the number of patterns that can be stored and accurately recalled. Here, we study quantum associative memory based on quantum annealing and apply it to the particle track classification. We focus on discrimination models based on Ising formulations of quantum associative memory model (QAMM) recall and quantum content-addressable memory (QCAM) recall. We characterize classification performance of these approaches as a function detector resolution, pattern library size, and detector inefficiencies, using the D-Wave 2000Q processor as a testbed. Discrimination criteria is set using both solution-state energy and classification labels embedded in solution states. We find that energy-based QAMM classification performs well in regimes of small pattern density and low detector inefficiency. In contrast, state-based QCAM achieves reasonably high accuracy recall for large pattern density and the greatest recall accuracy robustness to a variety of detector noise sources.

The PROSPECT and STEREO collaborations present a combined measurement of the pure ^{235}U antineutrino spectrum, without site specific corrections or detector-dependent effects. The spectral measurements of the two highest precision experiments at research reactors are found to be compatible with χ^{2}/ndf=24.1/21, allowing a joint unfolding of the prompt energy measurements into antineutrino energy. This ν[over ¯]_{e} energy spectrum is provided to the community, and an excess of events relative to the Huber model is found in the 5-6 MeV region. When a Gaussian bump is fitted to the excess, the data-model χ^{2} value is improved, corresponding to a 2.4σ significance.

Quantum computing offers a new paradigm for advancing high-energy physics research by enabling novel methods for representing and reasoning about fundamental quantum mechanical phenomena. Realizing these ideals will require the development of novel computational tools for modeling and simulation, detection and classification, data analysis, and forecasting of high-energy physics (HEP) experiments. While the emerging hardware, software, and applications of quantum computing are exciting opportunities, significant gaps remain in integrating such techniques into the HEP community research programs. Here we identify both the challenges and opportunities for developing quantum computing systems and software to advance HEP discovery science. We describe opportunities for the focused development of algorithms, applications, software, hardware, and infrastructure to support both practical and theoretical applications of quantum computing to HEP problems within the next 10 years.

Interoperability between different organizations is a complex task, where a key element is to be able to define without ambiguity the concepts that are involved in each domain and their relations.A key aspect for enabling e-government is the technological support for complex interaction scenarios, defining collaborative Business Processes (BPs) that are the basis for these interactions.E-government collaborative BPs involve several and heterogeneous participants: organizations, partners, and users, with different capabilities, needs, and available technical support.The goal of this paper is to present ongoing research on e-government cross-organizational collaborative BPs support in a service-based interoperability platform.This proposal is focused on the formalization and exploitation of e-government knowledge and information (i.e., metamodels and ontologies) to improve the definition, automated generation, control, monitoring and improvement of e-government collaborative BPs.

method. We then apply the method to determine the 87Y(n, γ ) cross section, which has not been measured directly. The work was carried out in the context of an LLNL L2 Milestone. This report addresses the theory aspects of the milestone. A complementary document summarizes the experimental efforts [1].

We outline the physics opportunities provided by the Electron Ion Collider (EIC). These include the study of the parton structure of the nucleon and nuclei, the onset of gluon saturation, the production of jets and heavy flavor, hadron spectroscopy and tests of fundamental symmetries. We review the present status and future challenges in EIC theory that have to be addressed in order to realize this ambitious and impactful physics program, including how to engage a diverse and inclusive workforce. In order to address these many-fold challenges, we propose a coordinated effort involving theory groups with differing expertise is needed. We discuss the scientific goals and scope of such an EIC Theory Alliance.

In machine learning, overparameterization is associated with qualitative changes in the empirical risk landscape, which can lead to more efficient training dynamics. For many parameterized models used in statistical learning, there exists a critical number of parameters, or model size, above which the model is constructed and trained in the overparameterized regime. There are many characteristics of overparameterized loss landscapes. The most significant is the convergence of standard gradient descent to global or local minima of low loss. In this work, we study the onset of overparameterization transitions for quantum circuit Born machines, generative models that are trained using non-adversarial gradient-based methods. We observe that bounds based on numerical analysis are in general good lower bounds on the overparameterization transition. However, bounds based on the quantum circuit's algebraic structure are very loose upper bounds. Our results indicate that fully understanding the trainability of these models remains an open question.

Background. Higher air pollution emissions can be observed near rail networks, local and highway automobile corridors, and shipyards. Communities near such sources are often disproportionately exposed to emissions from these stationary and mobile sources. One such community is West San Bernardino in California, where households are feet away from the Burlington Northern Santa Fe intermodal facility and are impacted by activities that are estimated to continuously emit air pollutants due to 24/7 operation.Objective. This study aimed to (1) quantify the impact of personal mobility and housing characteristics on daily PM2.5 exposures and well-being for West San Bernardino community members, and (2) develop individualized resilience plans for community collaborators to support future PM2.5 exposure reduction. Methods.Personal PM2.5 exposures were measured for each community collaborator for seven consecutive days during three deployment periods: October 2021, January 2022, and March 2022. Indoor and ambient PM2.5 levels were also continuously measured for five households over six months using PurpleAir Classic monitors. Demographic and well-being data were collected upon recruitment and after each week of engagement, respectively. Results.Personal exposures in home microenvironments were highest near the railyard and decreased with distance from the railyard. Home exposures were 40% higher on average compared to non-home microenvironments. Household PM2.5 levels had a higher-than-expected average infiltration factor of 0.70, and indoor 98th percentiles across the households far exceeded a healthy level at an average of 61 μg/m3. Increasing median personal exposures were linearly correlated with worsening health conditions.Significance.Results suggest that surrounding land use, household building characteristics, and indoor activity all compound to worsen air pollution exposures beyond what is expected for exposures in non-industrialized areas. Findings prompt a call for stronger regulation, not only for emissions, but also for indoor air quality and zoning standards that specifically protect disproportionately impacted communities.

It is our distinct pleasure and honor to welcome you all to the Fourth IEEE International Conference on Quantum Computing and Engineering (QCE23), also known as IEEE Quantum Week 2023. With your outstanding contributions and participation, QCE23 offers valuable opportunities to interact with experts in a full range of quantum technologies, from quantum device engineering to quantum computing and applications.

It is our distinct pleasure and honor to welcome you all to the Fourth IEEE International Conference on Quantum Computing and Engineering (QCE23), also known as IEEE Quantum Week 2023. With your outstanding contributions and participation, QCE23 offers valuable opportunities to interact with experts in a full range of quantum technologies, from quantum device engineering to quantum computing and applications.

Genomic analysis of the marine alga Corallina chilensis from Tomales Bay, California, USA, resulted in the assembly of its complete mitogenome (GenBank accession number MK598844) and plastid genome (GenBank MK598845). The mitogenome is 25,895 bp in length and contains 50 genes. The plastid genome is 178,350 bp and contains 233 genes. The organellar genomes share a high-level of gene synteny to other Corallinales. Comparison of rbcL and cox1 gene sequences of C. chilensis from Tomales Bay reveals it is identical to three specimens from British Columbia, Canada and very similar to a specimen of C. chilensis from southern California. These genetic data confirm that C. chilensis is distributed in Pacific North America.

In the last decades the conceptual and technological support for e-government initiatives have evolved from simple websites where news and links to e-government organizations and documents were posted, to complex inter-organizational systems platforms providing dynamic support for collaborative business processes (CBPs) and interoperability within e-government organizations, users and partners.Business Process Management (BPM) deals with the process lifecycle and technologies in organizations willing to drive their business based on the underlying processes they perform, to provide services or products with value for end users.Although many advances have been made in both the foundations of BPM and the technological platforms supporting the enactment of processes, e-government collaborative process present several challenges to be yet addressed.

Digital Government (e-Government) provides support for processes, activities and e-government resources within organizations involved through information and communication technology (ICT), focusing on value delivery to citizens. Collaborative business processes span several organizations, with different actors and heterogeneous technologies and systems, leading to complex interactions within different e-Government models and available technologies. Business Process Management (BPM) provides support for the business processes lifecycle, defining phases and activities to provide services or products with value for end users. Successful inter-organizational processes management and enactment within e-Government collaborative organizations will lead to better conceptual and technological integration, not only with each other but with citizens and users in general.Although in the last decades many advances have been made in the integration of BPM foundations and technological platforms to e-Government settings, several challenges still remain open.


 
 
 Since the beginning of 2020, COVID-19 has had a strong impact on the health of the world population. The mostly used approach to stop the epidemic is the application of controls of a classic epidemic such as case isolation, contact monitoring, and quarantine, as well as physical distancing and hygienic measures. Tracing the contacts of infected people is one of the main strategies for controlling the pandemic. Manual contact tracing is a slow, error-prone (by omission or forgotten) process, and vulnerable in terms of security and privacy. Furthermore, it needs to be carried out by specially trained personnel and it is not effective in identifying contacts with strangers (for example in public transport, supermarkets, etc). Given the high rates of contagion, which makes difficult an effective manual contact tracing, multiple initiatives arose for developing digital proximity tracing technologies. In this paper, we discuss in depth the security and personal data protection requirements that these technologies must satisfy, and we present an exhaustive and detailed list of the various applications that have been deployed globally, as well as the underlying infrastructure models and technologies they used. In particular, we identify potential threats that could undermine the satisfaction of the analyzed requirements, violating hegemonic personal data protection regulations.
 
 

Three Latina computing professionals at a large national laboratory reflect on the circumstances affecting the low representation of this segment of the population in STEM fields, and computing in particular. The authors share highlights of their path to STEM careers, and some of the efforts they are involved in for broadening participation in computing. They consider the roles of minority serving institutions, representation and mentoring, and advocacy.

The underlying premise of process management is that the quality of products and services is largely determined by the quality of the processes used to develop, deliver and support them. A concept which has been closely related to process quality over the last few years is the maturity of the process and it is important to highlight the current proposal of Business Process Maturity Model (BPMM), which is based on the principles, architecture and practices of CMM and CMMI for Software and describes the essential practices for the development, preparation, deployment, operations and support of product and service offers from determining customer needs. When maturity models are in place, it is important not to forget the important role that measurement can play, being essential in organizations which intend to reach a high level in the maturity in their processes. This is demonstrated by observing the degree of importance that measurement activities have in maturity models. This chapter tackles the Business Process Maturity Model and the role that business measurement plays in the context of this model. In addition, a set of representative business process measures aligned with the characteristics of BPMM are introduced which can guide organizations to support the measurement of their business processes depending on their maturity.Request access from your librarian to read this chapter's full text.

This article is addressed to develop a general procedure for the prediction of fouling in the holding tube of plate heat exchangers to different pHs. A practical procedure based on the combination of a non-linear function and sequential annealing (SA) algorithm allows predicting the fouled deposit thickness. Good agreement of the predicted results and experimental data has been achieved. The results are encouraging enough to validate current operating industrial techniques and can be extended to other type of equipments or heat exchangers.

<p>Between the second semester of 2009 and the first semester of 2011, camera traps were set up in conserved and disturbed habitats in the Otún Quimbaya Flora and Fauna Sanctuary. From a sampling effort of 2,066 camera-days, 673 photographs of 157 independent events were obtained for eight species of wild mammals and a domestic one. Their activity patterns were mainly nocturnal even for those species reported as diurnal. The impact of human interference and exotic species was evident for two species: <em>Tapirus pinchaque</em> and <em>Cerdocyon thous</em>. The former was observed below its altitudinal range with activity patterns mainly crepuscular and nocturnal. The second was observed in the same habitats where domestic dogs were found, with activity patterns mainly crepuscular and nocturnal. These findings suggest that both species have altered their activity patterns. Actions must be focused on decreasing the interaction of these mammals with humans and domestic dogs.</p>

At the Escuela Politécnica Nacional we have assembled a WCD (Water Cherenkov Detector) prototype for the LAGO (Latin American Giant Observatory) project in Ecuador. This article presents the data as well as the analysis corresponding to October, 2015. We present the obtained Charge Distribution Histogram (CDH). We shaped the conditions in which the equipment is operating given the environmental parameters and the value for the first VEM (Vertical Equivalent Muon) for the "Politanque".

Review of:
 Buying Into the Regime: Grapes and Consumption in Cold War Chile and the United States, Heidi Tinsman. (Durham: Duke University Press, 2014. 363 pp.) ISBN 978-0-8223-5535-9. Price $28.95
 
 The Chicken and the Quetzal: Incommensurate Ontologies and Portable Values in Guatemala’s Cloud Forest, Paul Kockelman. (Durham: Duke University Press, 2016. 190 pp.) ISBN 978-0-8223-6072-8. Price $23.95

Unsupervised training of generative models is a machine learning task that has many applications in scientific computing. In this work we evaluate the efficacy of using quantum circuit-based generative models to generate synthetic data of high energy physics processes. We use non-adversarial, gradient-based training of quantum circuit Born machines to generate joint distributions over 2 and 3 variables.

The PROSPECT experiment has substantially addressed the original 'Reactor Antineutrino Anomaly' by performing a high-resolution spectrum measurement from an enriched compact reactor core and a reactor model-independent sterile neutrino oscillation search based on the unique spectral distortions the existence of eV$^2$-scale sterile neutrinos would impart. But as the field has evolved, the current short-baseline (SBL) landscape supports many complex phenomenological interpretations, establishing a need for complementary experimental approaches to resolve the situation. While the global suite of SBL reactor experiments, including PROSPECT, have probed much of the sterile neutrino parameter space, there remains a large region above 1 eV$^2$ that remains unaddressed. Recent results from BEST confirm the Gallium Anomaly, increasing its significance to $\sim 5\sigma$, with sterile neutrinos providing a possible explanation of this anomaly. Separately, the MicroBooNE exclusion of electron-like signatures causing the MiniBooNE low-energy excess does not eliminate the possibility of sterile neutrinos as an explanation. Focusing specifically on the future use of reactors as a neutrino source for beyond-the-standard-model physics and applications, higher-precision spectral measurements still have a role to play. These recent results have created a confusing landscape which requires new data to disentangle the seemingly contradictory measurements. To directly probe $\overline{\nu}_{e}$ disappearance from high $\Delta m^2$ sterile neutrinos, the PROSPECT collaboration proposes to build an upgraded and improved detector, PROSPECT-II. It features an evolutionary detector design which can be constructed and deployed within one year and have impactful physics with as little as one calendar year of data.

Quantum computing holds the promise of substantially speeding up computationally expensive tasks, such as solving optimization problems over a large number of elements. In high-energy collider physics, quantum-assisted algorithms might accelerate the clustering of particles into jets. In this study, we benchmark quantum annealing strategies for jet clustering based on optimizing a quantity called "thrust" in electron-positron collision events. We find that quantum annealing yields similar performance to exact classical approaches and classical heuristics, but only after tuning the annealing parameters. Without tuning, comparable performance can be obtained through a hybrid quantum/classical approach.

Some of the most significant achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and operating large-scale experiments like the Large Hadron Collider or the Tevatron. In these facilities, the ultimate theory to describe matter at the most fundamental level is constantly probed and verified. These experiments often produce large amounts of data that require storing, processing, and analysis techniques that continually push the limits of traditional information processing schemes. Thus, the High-Energy Physics (HEP) field has benefited from advancements in information processing and the development of algorithms and tools for large datasets. More recently, quantum computing applications have been investigated to understand how the community can benefit from the advantages of quantum information science. Nonetheless, to unleash the full potential of quantum computing, there is a need to understand the quantum behavior and, thus, scale up current algorithms beyond what can be simulated in classical processors. In this work, we explore potential applications of quantum machine learning to data analysis tasks in HEP and how to overcome the limitations of algorithms targeted for Noisy Intermediate-Scale Quantum (NISQ) devices.

organizations facing the challenges of extracting knowledge from distributed multi-petabyte data stores. A central concept in this development program is a new paradigm ‘consistent network operations’ among widely distributed computing and storage facilities, where stable high throughput flows, at set rates, cross load-balanced network paths, up to flexible high water marks that are adjusted in real time to accommodate other network traffic. The large smooth flows are launched and managed by SDN services that act in concert with the experiments’ site-resident data distribution and management systems, to meet the expanding needs of the science programs. The technical goals include the construction of autonomous, intelligent site-resident services that interact dynamically with network-resident services, and with the science programs’ principal data distribution and management tools, to request or command network resources in support of high throughput petascale to exascale workflow Specific work items include: 1. Developing compact Data Transfer Nodes (DTNs) with auto-tuning functions that support data transfer rates in the 100 Gbps to the 1 Tbps range when used with high throughput data transfer applications 2. Deep site orchestration among virtualized clusters, storage subsystems and subnets to successfully coschedule CPU, storage and network resources 3. Science-program designed site architectures, operational modes, and policy and resource usage priorities, adjudicated across multiple network domains and virtual organizations, using the orchestration functions and methods 4. Seamlessly extending end-to-end operation across both extra-site and intra-site boundaries through the use of Open vSwitch (OVS), FireQoS + next generation Science DMZs 5. Novel methods of system integration that enable granular control of extreme scale long distance transfers through flow matching of scattered source-destination address pairs to multi-domain dynamic circuits 6. Funneling massive sets of streams to DTNs at the site edge hosting petascale buffer pools configured for flows of 100 Gbps and up, exploiting state of the art data transfers 7. Adaptive scheduling based on pervasive end-to-end monitoring, including DTN or compute-node resident agents providing comprehensive end-system profiling 8. Developing unsupervised and supervised machine learning and modeling methods to optimize the workflow involving terabyte to multi-petabyte datasets.

Esta publicacion fue realizada en el marco del proyecto Derribando barreras : Por mas mujeres en las areas STEM, que llevo adelante la Facultad de Ciencias Sociales y la Facultad de Ingenieria (Instituto de Computacion) de la Universidad de la Republica (UdelaR) financiado por la Comision Sectorial de Investigacion Cientifica (CSIC) a traves del Fondo universitario para contribuir a la comprension publica de temas de interes general (Art 2).

This chapter reenacts the collective presentation given by five graduate students at a roundtable discussion that took place on their campus. From that day, the scholars have continued to ask, “Who is listening?” when the underrepresented speak back to power. These students mark the institution’s inability to hear them as indicative of a broader problem within academia. While the roundtable discussion did not directly impact the larger structures that constrain the students’ academic experiences, they have managed to forge interpersonal bonds and a counter-network as sustainable pathways forward towards enacting the greater change they seek: the deployment of polyvocal narratives, collective co-witnessing, and spectral modes of engagement.

This project explored the use of quantum-assisted algorithms for pattern matching in sub-atomic physics experiments. Pattern matching algorithms are commonly employed to prune data of random noise and to help discriminate between signals generated by particle tracks of interest and signals generated by background events. The quantum-assisted algorithms explored in this project were based on an Ising formulation of quantum associative model (QAMM) recall and quantum content-addressable memory (QCAM) recall. The recall is performed by comparing a probe pattern with those stored in a library of patterns encoded in the QAMM/QCAM model. The classification accuracy of QAMM and QCAM recall was determined as a function of detector resolution, noise, and efficiency and pattern density, where pattern density is defined as the ratio of the number of reference signal patterns encoded in the library to each pattern’s length. We found that QAMM achieved high classification accuracy when applied to datasets with low pattern density. QCAM achieved high classification accuracy for datasets with high pattern density and was found to be more robust to detector noise. The project methodology and results are described in detail in our arXiv preprint (arXiv:2011.11848) . This project was conducted by scientists at the Johns Hopkins University Applied Physics Laboratory and Oak Ridge National Laboratory from August 2018 to August 2020 and was supported by DOE grant DE-SC0019497.

Supplementary Material for the paper "Contact tracing solutions for COVID-19: applications, data privacy and security"

Summary form only given. Cr:forsterite is a vibronic laser source tunable in the near-infrared spectral region in the range 1.15-1.35 /spl mu/m. In this paper, we report for the first time, to our knowledge, on the operation of a regenerative amplifier system based upon Cr:forsterite. Self-mode-locked Cr:forsterite lasers have generated pulses as short as 25 fs, theoretically the large bandwidth of the Cr:forsterite laser emission can support sub-20-fs pulses. To minimize optical damage in the amplifier rod we have utilized the technique of chirped pulse amplification (CPA).