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1d_harmonic_oscillator

1D quantum harmonic oscillator hamiltonian

3d_fermi_hubbard_model

3D_Fermi_Hubbard_Model

auto-gpt

An experimental open-source attempt to make GPT-4 fully autonomous.

berry_phase_semimetal

conductivity-visualization

Set of codes used to visualize spatial conductivity in the paper "Spatial Projection of Electronic Conductivity: The Example of Conducting Bridge Memory Materials

decodense

decodense: Bond- and atom-wise decompositions of HF and KS-DFT calculations

deep-implicit-attention

Implementation of deep implicit attention in PyTorch

dpll

A collection of phase locked loop (PLL) related projects

entmax

The entmax mapping and its loss, a family of sparse softmax alternatives.

espresso

The ESPResSo package

extendedkalmanfilter

Extended Kalman Filter in Verilog

f22-projects

Links to all ten AI Club projects in Fall 2022. All projects will automatically enter a semester-long hackathon and compete for prizes.

final-project

It is the Final project

fpga-super-mario

This is an implementation in Verilog of Super Mario Bros console game levels to be uploaded in a FPGA card.

frib-tasummerschoolquantumcomputing

Recent developments in quantum information systems and technologies offer the possibility to address some of the most challenging large-scale problems in science, whether they are represented by complicated interacting quantum mechanical systems or classical systems. The last years have seen a rapid and exciting development in algorithms and quantum hardware. The emphasis of this summer school is to highlight, through a series of lectures and hands-on exercises and practice sessions, how quantum computing algorithms can be used to study nuclear few- and many-body problems of relevance for low-energy nuclear physics. And how quantum computing algorithms can aid in studying systems with increasingly many more degrees of freedom compared with more classical few- and many-body methods. Several quantum algorithms for solving quantum-mechanical few- and many-particle problems with be discussed. The lectures will start with the basic ideas of quantum computing. Thereafter, through examples from nuclear physics, we will elucidate how different quantum algorithms can be used to study these systems. The results from various quantum computing algorithms will be compared to standard methods like full configuration interaction theory, field theories on the lattice, in-medium similarity renormalization group and coupled cluster theories.

gated-graph-neural-network-samples

Sample Code for Gated Graph Neural Networks

gnn

Use GNN for predicting molecular properties and energy

heic_to_pdf

converting HEIC to PDF file

hw

HW PHY480/905

intento_task

Code used to clean the data

josephson_junction_plot_fitting

Plot fitting method for josephson junction

keithley

Keithley Instruments reference and support code

labdrivers

Python drivers for lab hardware

lock_in_pll

manim

Animation engine for explanatory math videos

manybody

Many Body using neural networks

mechkit

Continuum mechanics toolkit

mpnn

Open source implementation of "Neural Message Passing for Quantum Chemistry"

nematic-learning

Repository for "Machine learning microscopic form of nematic order in twisted double-bilayer graphene"

nuclear-quantum-computing