Deep quantum neural networks equipped with backpropagation on a superconducting processor

Deep learning and quantum computing have achieved dramatic progresses in recent years. The interplay between these two fast-growing fields gives rise to a new research frontier of quantum machine learning. In this work, we report the first experimental demonstration of training deep quantum neural networks via the backpropagation algorithm with a six-qubit programmable superconducting processor. In particular, we show that three-layer deep quantum neural networks can be trained efficiently to learn two-qubit quantum channels with a mean fidelity up to 96.0% and the ground state energy of molecular hydrogen with an accuracy up to 93.3% compared to the theoretical value. In addition, six-layer deep quantum neural networks can be trained in a similar fashion to achieve a mean fidelity up to 94.8% for learning single-qubit quantum channels. Our experimental results explicitly showcase the advantages of deep quantum neural networks, including quantum analogue of the backpropagation algorithm and less stringent coherence-time requirement for their constituting physical qubits, thus providing a valuable guide for quantum machine learning applications with both near-term and future quantum devices.Comment: 7 pages (main text) + 11 pages (Supplementary Information), 10 figure

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Deterministic-aided single dataset STAP method based on sparse recovery in heterogeneous clutter environments

Abstract Traditional space-time adaptive processing (STAP) usually needs many independent and identically distributed (i.i.d) training datasets for estimating clutter covariance matrix (CCM). But this requirement is hardly satisfied in the heterogeneous clutter environments, which lead to an inaccurate estimation of CCM and accordingly degrade the performance of STAP significantly. To improve the performance of STAP in heterogeneous environments, a novel deterministic-aided (DA) single dataset STAP method based on sparse recovery technique (SR) is proposed in this paper. This presented algorithm exploits the property that the clutter components of side-looking airborne or spaceborne radar are distributed along the clutter ridge to estimate the CCM of the cell under test (CUT) without any secondary training data. The new method only uses a single CUT data to acquire a high-resolution angle-Doppler power spectrum using sparse recovery (SR) approach and then employs a new adaptive deterministic-aided generalized inner product (GIP) algorithm to recognize and select the clutter components in the CUT angle-Doppler power spectrum automatically. Subsequently, the CCM, which is used to construct the weights of STAP filter, can be effectively estimated by the selected clutter components to fulfill the final STAP filter processing. Simulation results verify the effectiveness of the proposed detection method

Experimental Study of Influence of Core Wettability on Imbibition Properties

Through new core wettability simulation technology and the single-sided unidirectional imbibition experimental method, the influence of core wettability on oil imbibition characteristics was studied by using artificial cores with wettability index in the range of −0.9~0.95. Results show that for the cores with permeability from ultra-low to medium–high, the imbibition time shows a monotonically decreasing law with the increase in the wettability index. In the weak water-wet range, the imbibition time increases significantly with the weakening of water-wet. Oil imbibition rate goes up with the increase in wettability index. In the strong water-wet range, the imbibition rate will change significantly with wettability. In the water-wet zone, there is a positive correlation between imbibition oil limit recovery and wettability index, according to which a power exponent model of them is established. The imbibition–displacement ratio, which characterizes the contribution rate of oil recovery by imbibition to that by waterflooding, is also positively correlated with the wettability index. In addition, imbibition–displacement ratios of extra-low permeability cores are very close to that of medium–high permeability cores. According to the analysis of the research results, compared with the strongly water-wet oil layer, the weakly water-wet oil layer with a wettability index of 0–0.5 has a greater contribution to oil recovery by using the enhanced imbibition method

STAP Based on Toeplitz Covariance Matrix Reconstruction for Airborne Radar

We consider the problem of clutter covariance matrix (CCM) estimation for space-time adaptive processing (STAP) radar in the small sample. In this paper, a fast efficient algorithm for CCM reconstruction is proposed to overcome this shortcoming for the linear structure. Particularly, we present a low-rank matrix recovery (LRMR) question about CCM estimation based on the Toeplitz structure of CCM and the prior knowledge of the noise. The closed-form solution is obtained by relaxing the nonconvex LRMR problem that the trace norm replaces the rank norm. The target can then be efficiently detected by using the recovered CCM according to the STAP theorem. We also analyze the algorithm model under the linear structure in the presence of unknown mutual coupling. It is shown that our method can obtain accurate CCM in the small sample, with even higher accuracy than the traditional algorithms in the same number of samples. It also can reduce the coupling effect and obtain more degrees of freedom (DOF) with limited sensors and pulses by utilizing sparse linear structure (SLS) and improve angle and Doppler resolutions. Finally, numerical simulations have verified the effectiveness of the proposed method in comparison with some of the existing methods

A Novel MIMO Array with Reduced Mutual Coupling and Increased Degrees of Freedom

In this paper, we consider the problem of array design for Multiple-Input Multiple-Output (MIMO) array under the condition of fixed number of physical sensors and mutual coupling. A novel MIMO array based on the second-order super nested transmit and receive arrays is proposed by using the difference coarray. It can obtain the closed form expressions for the physical sensor locations and the degrees of freedom (DOF) from any given number physical sensors. The proposed array structure can significantly enhance DOF and effectively decrease unknown mutual coupling effect. The effectiveness and superiority of the proposed MIMO array structure are verified from the number of DOF and MUSIC spectra by numerical simulations

Fabrication and mechanical behavior of 2D-Cf/TaxHf1−xC–SiC composites by a low-temperature and highly-efficient route

Cf/TaxHf1−xC–SiC composites are ideal thermal structural materials for service under extreme conditions of hypersonic vehicles. However, how to synthesize TaxHf1-xC powders and efficiently fabricate Cf/TaxHf1-xC–SiC composites still faces some challenges. Furthermore, mechanical properties and thermophysical properties of TaxHf1−xC vary with the composition, but not monotonically. In-depth analysis of mechanical behaviors of the Cf/TaxHf1−xC–SiC composites is extremely important for their development and applications. In this study, the TaxHf1−xC powders (x = 0.2, 0.5, 0.8) were successfully synthesized via solid solution of TaC and HfC at a relatively low temperature of 1800 ℃, with a small amount of Si as an additive. Subsequently, the efficient fabrication of 2D-Cf/TaxHf1–xC–SiC composites was achieved by slurry impregnation and lamination (SIL) combined with precursor infiltration and pyrolysis (PIP). In addition, the mechanical behavior of the composites was investigated systematically. It is demonstrated that the composites present remarkable non-brittle fractures, including a large number of fiber pull out and interphase debonding. Also, the fracture failure involves a complex process of microcrack generation and propagation, matrix cracking, and layer fracture. Moreover, the interfacial bonding between the fibers and the matrix is enhanced as the Ta∶Hf ratio decreases from 4∶1 to 1∶4. As a result, Cf/Ta0.2Hf0.8C–SiC composites exhibit exceptional flexural strength of 437±19 MPa, improved by 46% compared with Cf/Ta0.8Hf0.2C–SiC (299±19 MPa). This study provides a new perception of design and fabrication of ultra-high-temperature ceramic (UHTC) matrix composites with high performance

Adsorption of Uranium(VI) from a Simulated Saline Solution by Alkali-Activated Leather Waste

A porous adsorbent was prepared from leather waste by activation with alkali. The adsorbent, alkali-activated leather waste (AALW), was applied to adsorb uranium­(VI) and characterized by scanning electron microscopy, energy-dispersive X-ray detection, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy. The influence of the pH, initial uranium­(VI) concentration, temperature, and contact time on the adsorption of uranium­(VI) was systematically investigated. The adsorption of uranium­(VI) on AALW obeyed the Langmuir isotherm model and was attributed to ion exchange and complexation coordination. Thermodynamic and kinetic studies showed that the adsorption process was spontaneous and endothermic, and it reached adsorption equilibrium in 360 min. Moreover, the selective adsorption of uranium­(VI) from an aqueous solution containing coexisting ions and adsorption of trace uranium­(VI) from a simulated high-salinity environment showed that AALW had not only a strong affinity but a high selectivity for uranium­(VI)