EinsteinPy: A Community Python Package for General Relativity

22 May 2020einsteinpy/einsteinpy

Python is a free, easy to use a high-level programming language which has seen a huge expansion in the number of its users and developers in recent years.

GENERAL RELATIVITY AND QUANTUM COSMOLOGY COSMOLOGY AND NONGALACTIC ASTROPHYSICS INSTRUMENTATION AND METHODS FOR ASTROPHYSICS 83-04

325
0.09 stars / hour

Investigating Quantum Approximate Optimization Algorithms under Bang-bang Protocols

The quantum approximate optimization algorithm (QAOA) is widely seen as a possible usage of noisy intermediate-scale quantum (NISQ) devices.

QUANTUM PHYSICS

15,372
0.06 stars / hour

A Feasible Approach for Automatically Differentiable Unitary Coupled-Cluster on Quantum Computers

11 Nov 2020aspuru-guzik-group/tequila

We show that, within our framework, the gradient of an expectation value with respect to a parameterized n-fold fermionic excitation can be evaluated by four expectation values of similar form and size, whereas most standard approaches based on the direct application of the parameter-shift-rule come with an associated cost of O(2^(2n)) expectation values.

QUANTUM PHYSICS CHEMICAL PHYSICS COMPUTATIONAL PHYSICS

141
0.05 stars / hour

RGBeta: A Mathematica Package for the Evaluation of Renormalization Group $β$-Functions

20 Jan 2021aethomsen/rgbeta

In completely generic four-dimensional gauge-Yukawa theories, the renormalization group $\beta$-functions are known to the 3-2-2 loop order in gauge, Yukawa, and quartic couplings, respectively.

HIGH ENERGY PHYSICS - PHENOMENOLOGY

6
0.04 stars / hour

Purely data-driven medium-range weather forecasting achieves comparable skill to physical models at similar resolution

19 Aug 2020raspstephan/weather-benchmark

Numerical weather prediction has traditionally been based on physical models of the atmosphere.

ATMOSPHERIC AND OCEANIC PHYSICS

216
0.04 stars / hour

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights.

BAND GAP FORMATION ENERGY MATERIALS SCIENCE

204
0.03 stars / hour

Multi-fidelity Graph Networks for Machine Learning the Experimental Properties of Ordered and Disordered Materials

9 May 2020materialsvirtuallab/megnet

Predicting the properties of a material from the arrangement of its atoms is a fundamental goal in materials science.

MATERIALS SCIENCE DISORDERED SYSTEMS AND NEURAL NETWORKS

227
0.03 stars / hour

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

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

227
0.03 stars / hour

Open Quantum Assembly Language

11 Jul 2017IBM/qiskit-openqasm

This document describes a quantum assembly language (QASM) called OpenQASM that is used to implement experiments with low depth quantum circuits.

QUANTUM PHYSICS

545
0.03 stars / hour

Almost-linear time decoding algorithm for topological codes

19 Sep 2017chaeyeunpark/UnionFind

Our algorithm has a worst case complexity of $O(n \alpha(n))$, where $n$ is the number of physical qubits and $\alpha$ is the inverse of Ackermann's function, which is very slowly growing.

QUANTUM PHYSICS

3
0.03 stars / hour