Trending Research
Deep-Learning Density Functional Theory Hamiltonian for Efficient ab initio Electronic-Structure Calculation
mzjb/deeph-pack
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The marriage of density functional theory (DFT) and deep learning methods has the potential to revolutionize modern computational materials science.
Materials Science Disordered Systems and Neural Networks Mesoscale and Nanoscale Physics Computational Physics Quantum Physics
The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design
The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques.
Materials Science Computational Physics
TensorCircuit: a Quantum Software Framework for the NISQ Era
tencent-quantum-lab/tensorcircuit
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TensorCircuit is an open source quantum circuit simulator based on tensor network contraction, designed for speed, flexibility and code efficiency.
Quantum Physics Computational Physics
Investigating Quantum Approximate Optimization Algorithms under Bang-bang Protocols
google-research/google-research
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The quantum approximate optimization algorithm (QAOA) is widely seen as a possible usage of noisy intermediate-scale quantum (NISQ) devices.
Quantum Physics
Symmetric projected entangled-pair states analysis of a phase transition in coupled spin-1/2 ladders
Infinite projected entangled-pair states (iPEPS) have been introduced to accurately describe many-body wave functions on two-dimensional lattices.
Strongly Correlated Electrons
Purely data-driven medium-range weather forecasting achieves comparable skill to physical models at similar resolution
ai4environment/WeatherBench
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Numerical weather prediction has traditionally been based on physical models of the atmosphere.
Atmospheric and Oceanic Physics
A Grand Unification of Quantum Algorithms
ichuang/pyqsp
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Paralleling GSLW'19, we then employ QSVT to construct intuitive quantum algorithms for search, phase estimation, and Hamiltonian simulation, and also showcase algorithms for the eigenvalue threshold problem and matrix inversion.
Quantum Physics
Quantum Augmented Dual Attack
We present a quantum augmented variant of the dual lattice attack on the Learning with Errors (LWE) problem, using classical memory with quantum random access (QRACM).
Quantum Physics Cryptography and Security Data Structures and Algorithms
Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine-learning molecular dynamics simulations
Amorphous silicon (a-Si) is an important thermal-management material and also serves as an ideal playground for studying heat transport in strongly disordered materials.
Materials Science Computational Physics
Promoting global stability in data-driven models of quadratic nonlinear dynamics
Second, we illustrate how to modify the objective function in machine learning algorithms to promote globally stable models, with implications for the modeling of fluid and plasma flows.
Fluid Dynamics Computational Physics Plasma Physics
