Classical surrogates for quantum learning models

The advent of noisy intermediate-scale quantum computers has put the search for possible applications to the forefront of quantum information science. One area where hopes for an advantage through near-term quantum computers are high is quantum machine learning, where variational quantum learning models based on parametrized quantum circuits are discussed. In this work, we introduce the concept of a classical surrogate, a classical model which can be efficiently obtained from a trained quantum learning model and reproduces its input-output relations. As inference can be performed classically, the existence of a classical surrogate greatly enhances the applicability of a quantum learning strategy. However, the classical surrogate also challenges possible advantages of quantum schemes. As it is possible to directly optimize the ansatz of the classical surrogate, they create a natural benchmark the quantum model has to outperform. We show that large classes of well-analyzed re-uploading models have a classical surrogate. We conducted numerical experiments and found that these quantum models show no advantage in performance or trainability in the problems we analyze. This leaves only generalization capability as possible point of quantum advantage and emphasizes the dire need for a better understanding of inductive biases of quantum learning models.Comment: 4 pages, 3 figure

Thermodynamics of ligand binding to histone deacetylase like amidohydrolase from Bordetella/Alcaligenes

Thermodynamic studies on ligand–protein binding have become increasingly important in the process of drug design. In combination with structural data and molecular dynamics simulations, thermodynamic studies provide relevant information about the mode of interaction between compounds and their target proteins and therefore build a sound basis for further drug optimization. Using the example of histone deacetylases (HDACs), particularly the histone deacetylase like amidohydrolase (HDAH) from Bordetella/Alcaligenes, a novel sensitive competitive fluorescence resonance energy transfer-based binding assay was developed and the thermodynamics of interaction of both fluorescent ligands and inhibitors to histone deacetylase like amidohydrolase were investigated. The assay consumes only small amounts of valuable target proteins and is suitable for fast kinetic and mechanistic studies as well as high throughput screening applications. Binding affinity increased with increasing length of aliphatic spacers (n?=?4–7) between the hydroxamate moiety and the dansyl head group of ligand probes. Van't Hoff plots revealed an optimum in enthalpy contribution to the free energy of binding for the dansyl-ligand with hexyl spacer. The selectivity in the series of dansyl-ligands against human class I HDAC1 but not class II HDACs 4 and 6 increased with the ratio of deltaH0/deltaG0. The data clearly emphasize the importance of thermodynamic signatures as useful general guidance for the optimization of ligands or rational drug design

Improved Boreal Forest Wildfire Fuel Type Mapping in Interior Alaska Using AVIRIS-NG Hyperspectral Data

In Alaska the current wildfire fuel map products were generated from low spatial (30 m) and spectral resolution (11 bands) Landsat 8 satellite imagery which resulted in map products that not only lack the granularity but also have insufficient accuracy to be effective in fire and fuel management at a local scale. In this study we used higher spatial and spectral resolution AVIRIS-NG hyperspectral data (acquired as part of the NASA ABoVE project campaign) to generate boreal forest vegetation and fire fuel maps. Based on our field plot data, random forest classified images derived from 304 AVIRIS-NG bands at Viereck IV level (Alaska Vegetation Classification) had an 80% accuracy compared to the 33% accuracy of the LANDFIRE’s Existing Vegetation Type (EVT) product derived from Landsat 8. Not only did our product more accurately classify fire fuels but was also able to identify 20 dominant vegetation classes (percent cover \u3e1%) while the EVT product only identified 8 dominant classes within the study area. This study demonstrated that highly detailed and accurate fire fuel maps can be created at local sites where AVIRIS-NG is available and can provide valuable decision-support information to fire managers to combat wildfires

Characterization of Hydrogen Plasma Defined Graphene Edges

We investigate the quality of hydrogen plasma defined graphene edges by Raman spectroscopy, atomic resolution AFM and low temperature electronic transport measurements. The exposure of graphite samples to a remote hydrogen plasma leads to the formation of hexagonal shaped etch pits, reflecting the anisotropy of the etch. Atomic resolution AFM reveals that the sides of these hexagons are oriented along the zigzag direction of the graphite crystal lattice and the absence of the D-peak in the Raman spectrum indicates that the edges are high quality zigzag edges. In a second step of the experiment, we investigate hexagon edges created in single layer graphene on hexagonal boron nitride and find a substantial D-peak intensity. Polarization dependent Raman measurements reveal that hydrogen plasma defined edges consist of a mixture of zigzag and armchair segments. Furthermore, electronic transport measurements were performed on hydrogen plasma defined graphene nanoribbons which indicate a high quality of the bulk but a relatively low edge quality, in agreement with the Raman data. These findings are supported by tight-binding transport simulations. Hence, further optimization of the hydrogen plasma etching technique is required to obtain pure crystalline graphene edges.Comment: 10 pages, 7 figure

Using Synthetic Aperture Radar to Define Spring Breakup on the Kuparuk River, Northern Alaska

Spring runoff measurements of Arctic watersheds are challenging given the remote location and the often dangerous field conditions. This study combines remote sensing techniques and field measurements to evaluate the applicability of synthetic aperture radar (SAR) to defining spring breakup of the braided lower Kuparuk River, North Slope, Alaska. A statistical analysis was carried out on a time series (2001–10) of SAR images acquired from the European Remote-Sensing Satellite (ERS-2) and the Canadian RADARSAT satellite, as well as on measured runoff. On the basis of field information, the SAR images were separated into pre-breakup, breakup, and post-breakup periods. Three variables were analyzed for their suitability to bracket the river breakup period: image brightness, variance in brightness over the river length, and a sum of rank order change analysis. Variance in brightness was found to be the most reliable indicator. A combined use of that variance and sum of rank order change appeared promising when enough images were available. The temporal resolution of imagery served as the major limitation in constraining the timing of the hydrologic event. Challenges associated with spring runoff monitoring and the sensitive nature of SAR likely resulted in an earlier detection of surficial changes by the remote sensing technique compared to the field runoff observations. Given a sufficient temporal resolution, SAR imagery has the potential to improve the spatiotemporal monitoring of Arctic watersheds for river breakup investigations.La mesure de l’écoulement printanier des bassins hydrographiques de l’Arctique n’est pas facile à réaliser en raison de l’éloignement ainsi qu’en raison des conditions souvent dangereuses qui ont cours sur le terrain. Cette étude fait appel à des techniques de télédétection de même qu’aux mesures prises sur le terrain pour évaluer l’applicabilité du radar à synthèse d’ouverture SAR pour définir la débâcle printanière de la basse rivière Kuparuk anastomosée sur la North Slope de l’Alaska. L’analyse statistique d’une série temporelle (2001-2010) d’images SAR acquises à partir du satellite européen de télédétection (ERS-2) et du satellite canadien RADARSAT ainsi que des écoulements mesurés a été effectuée dans le cadre de cette étude. D’après les renseignements recueillis sur le terrain, les images SAR ont été divisées en fonction de la période précédant la débâcle, de la période de la débâcle même et de la période suivant la débâcle. Trois variables ont été analysées afin de déterminer si elles permettaient d’isoler la période de la débâcle de la rivière, soit la luminance de l’image, la variance de la luminance en fonction de la longueur de la rivière et la somme de l’analyse des changements de classement suivant le rang. La variance de la luminance s’est avérée l’indicateur le plus fiable. L’utilisation conjointe de cette variance et de la somme des changements de classement suivant le rang s’avéraient prometteuse lorsque le nombre d’images était suffisant. La résolution temporelle de l’imagerie a constitué la plus grande limitation pour contraindre la temporisation de l’événement hydrologique. Les défis liés à la surveillance de l’écoulement printanier et la nature sensible du SAR ont vraisemblablement donné lieu à la détection précoce des changements superficiels au moyen de la technique de télédétection comparativement aux observations mêmes de l’écoulement printanier. Moyennant une résolution temporelle suffisante, l’imagerie SAR pourrait permettre d’améliorer la surveillance spatiotemporelle des bassins hydrographiques de l’Arctique en vue de l’étude des débâcles printaniers

Estimating Snow Accumulation and Ablation with L-Band Interferometric Synthetic Aperture Radar (InSAR)

Snow is a critical water resource for the western United States and many regions across the globe. However, our ability to accurately measure and monitor changes in snow mass from satellite remote sensing, specifically its water equivalent, remains a challenge. To confront these challenges, NASA initiated the SnowEx program, a multiyear effort to address knowledge gaps in snow remote sensing. During SnowEx 2020, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) team acquired an L-band interferometric synthetic aperture radar (InSAR) data time series to evaluate the capabilities and limitations of repeat-pass L-band InSAR for tracking changes in snow water equivalent (SWE). The goal was to develop a more comprehensive understanding of where and when L-band InSAR can provide SWE change estimates, allowing the snow community to leverage the upcoming NASA–ISRO (NASA–Indian Space Research Organization) SAR (NISAR) mission. Our study analyzed three InSAR image pairs from the Jemez Mountains, NM, between 12 and 26 February 2020. We developed a snow-focused multi-sensor method that uses UAVSAR InSAR data synergistically with optical fractional snow-covered area (fSCA) information. Combining these two remote sensing datasets allows for atmospheric correction and delineation of snow-covered pixels within the radar swath. For all InSAR pairs, we converted phase change values to SWE change estimates between the three acquisition dates. We then evaluated InSAR-derived retrievals using a combination of fSCA, snow pits, meteorological station data, in situ snow depth sensors, and ground-penetrating radar (GPR). The results of this study show that repeat-pass L-band InSAR is effective for estimating both snow accumulation and ablation with the proper measurement timing, reference phase, and snowpack conditions

Effects of Relativistic Dynamics in pp→ppπ0pp \to pp \pi^0 near Threshold

The cross-section for threshold $\pi^0$ production in proton-proton collisions is evaluated in the framework of the covariant spectator description. The negative energy intermediate states are included non-perturbatively and seen to yield a considerably smaller contribution, when compared to perturbative treatments. A family of OBE-models with different off-shell scalar coupling is considered.Comment: 10 pages, 3 figures, 1 tabl

Investigations of Hail Damage Swaths Using Various Satellite Remote Sensing Platforms

Severe thunderstorms that bring damaging winds and large hail can cause significant damage to agricultural crops. Severe thunderstorms can cause upwards of several hundreds of millions of dollars in damage to agricultural areas. Formal ground surveys are not conducted on these areas of damage, like they are for suspected tornado damaged areas. IF ground surveys were conducted, they would likely be time and resources consuming due to their large spatial extent. Satellite remote sensing has been frequently used in identification and analysis of these hail damage swaths. Previous analysis have looked at the simple change in damaged vegetation to looking at the damage areas in satellite imagery with varying spatial resolutions. One study has even looked at the impacts that these damage swaths can have on the land surface, associated fluxes and how they affect numerical weather prediction. Previous studies have focused on using optical remote (VIS, NIR, SWIR) sensing instruments and derived indices, such as Normalized Difference Vegetation Index (NDVI) for analysis. NDVI is used to monitor the health (greenness) of the vegetation. Optical sensors however are limited by sky conditions over the areas they are imaging and certain bands are further limited by the diurnal cycle. These limitations can lead to sometimes upwards of 7 to 10 day gaps of the surface not being imaged, especially during the height of summer convection. One way to obtain more views of the surface, regardless of the sky conditions or time of day is through the use of synthetic aperture radar (SAR). SAR sensors are active instruments that transmit in the microwave portion of the EM spectrum. The surface and its characteristics will determine the amount of energy scattered back to the sensor. The SAR sensors then measure amplitude and phase of wavelength coming back from surface

Near Real-Time Use of Optical Remote Sensing and Synthetic Aperture Radar for Response to Central U.S. Flooding in Late April-Early May 2017

No abstract availabl