SeisBench -- A Toolbox for Machine Learning in Seismology

seisbench/seisbench 1 Nov 2021

Accessing these various benchmark datasets for training and implementing the standardization of models is currently a time-consuming process, hindering further advancement of ML techniques within seismology.


0.02 stars / hour

Mitiq: A software package for error mitigation on noisy quantum computers

unitaryfund/mitiq 9 Sep 2020

We introduce Mitiq, a Python package for error mitigation on noisy quantum computers.

Quantum Physics Emerging Technologies

0.02 stars / hour

Overcoming timestep limitations in boosted-frame Particle-In-Cell simulations of plasma-based acceleration

ECP-WarpX/WarpX 28 Apr 2021

In the case of boosted-frame PIC simulations of plasma-based acceleration, this limitation can be a major hinderance as the cells are often very elongated along the longitudinal direction and the timestep is thus limited by the small, transverse cell size.

Accelerator Physics Computational Physics

0.02 stars / hour

Quantum autoencoders with enhanced data encoding

Quantum-TII/qibo 13 Oct 2020

We present the enhanced feature quantum autoencoder, or EF-QAE, a variational quantum algorithm capable of compressing quantum states of different models with higher fidelity.

Quantum Physics Statistical Mechanics High Energy Physics - Theory

0.02 stars / hour

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

materialsvirtuallab/megnet Chem. Mater. 2018

Similarly, we show that MEGNet models trained on $\sim 60, 000$ crystals in the Materials Project substantially outperform prior ML models in the prediction of the formation energies, band gaps and elastic moduli of crystals, achieving better than DFT accuracy over a much larger data set.

Drug Discovery Formation Energy Materials Science Computational Physics

0.01 stars / hour

Optimized Low-Depth Quantum Circuits for Molecular Electronic Structure using a Separable Pair Approximation

tequilahub/tequila 9 May 2021

We present a classically solvable model that leads to optimized low-depth quantum circuits leveraging separable pair approximations.

Quantum Physics Chemical Physics Computational Physics

0.01 stars / hour

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

atomistic-machine-learning/schnetpack 4 Sep 2018

SchNetPack is a toolbox for the development and application of deep neural networks to the prediction of potential energy surfaces and other quantum-chemical properties of molecules and materials.

Computational Physics Chemical Physics

0.01 stars / hour

Entangling Quantum Generative Adversarial Networks

tensorflow/quantum 30 Apr 2021

Generative adversarial networks (GANs) are one of the most widely adopted semisupervised and unsupervised machine learning methods for high-definition image, video, and audio generation.

Quantum Physics

0.01 stars / hour

Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning

deepmodeling/deepmd-kit 1 May 2020

For 35 years, {\it ab initio} molecular dynamics (AIMD) has been the method of choice for modeling complex atomistic phenomena from first principles.

Computational Physics

0.01 stars / hour

Automated discovery of superconducting circuits and its application to 4-local coupler design

ljvmiranda921/pyswarms 6 Dec 2019

Superconducting circuits have emerged as a promising platform to build quantum processors.

Quantum Physics

0.01 stars / hour