Results 271 to 280 of about 1,412,537 (346)
physica status solidi (RRL) – Rapid Research Letters, EarlyView.Reservoir computing with optical devices offers an energy‐efficient approach for time‐series forecasting. For quantum‐dot lasers with optical self‐feedback, the scattering timescale R into the quantum‐dot levels changes the predictive performance of the reservoir computer.
Huifang Dong +2 more
wiley +1 more source
Detecting Signatures of Criticality Using Divergence Rate. [PDF]
Entropy (Basel)Chan T, Soh W, Hillar C.
europepmc +1 more source
Data assimilation errors due to incomplete accounting of balanced versus unbalanced moisture
Quarterly Journal of the Royal Meteorological Society, EarlyView.We investigate data assimilation strategies with moisture and assess the impact of accounting for moisture's balanced and unbalanced components. To illustrate the main ideas, examples are presented using a simple model consisting of linear, moist, shallow‐water equations with stochastic forcing.
H. Reed Ogrosky +3 more
wiley +1 more source
The quantum gravity seeds for laws of nature. [PDF]
Eur J Philos SciLam V, Oriti D.
europepmc +1 more source
A clockmaker's mathematics: a technology-based approach to the mathematical works of Jost Bürgi (1552-1632). [PDF]
Arch Hist Exact SciMoosbrugger D.
europepmc +1 more source
Advanced Quantum Technologies, EarlyView.The hybrid approach to Quantum Supervised Machine Learning is compatible with Noisy Intermediate Scale Quantum (NISQ) devices but hardly useful. Pure quantum kernels requiring fault‐tolerant quantum computers are more promising. Examples are kernels computed by means of the Quantum Fourier Transform (QFT) and kernels defined via the calculation of ...
Massimiliano Incudini +2 more
wiley +1 more source
Adversarial Range Gate Pull-Off Jamming Against Tracking Radar. [PDF]
Sensors (Basel)Wang Y, Han Y, Jiang Y.
europepmc +1 more source
What can we Learn from Quantum Convolutional Neural Networks?
Advanced Quantum Technologies, EarlyView.Quantum Convolutional Neural Networks have been long touted as one of the premium architectures for quantum machine learning (QML). But what exactly makes them so successful for tasks involving quantum data? This study unlocks some of these mysteries; particularly highlighting how quantum data embedding provides a basis for superior performance in ...
Chukwudubem Umeano +3 more
wiley +1 more source
Transcendental Brauer-Manin obstructions on singular K3 surfaces. [PDF]
Res Number TheoryAlaa Tawfik M, Newton R.
europepmc +1 more source
Reproducing the Effects of Quantum Deformation in the Undeformed Jaynes‐Cummings Model
Advanced Quantum Technologies, EarlyView.The inverse problem approach, where atomic probabilities are modulated according to a time‐dependent coupling, is studied for the Jaynes‐Cummings (JC) model. In particular, emphasis is placed on how to reproduce the effects of quantum deformation in a non‐deformed JC model.
Thiago T. Tsutsui +2 more
wiley +1 more source