Teinkkkkkkkkkkk's Projects
1D quantum harmonic oscillator hamiltonian
3D_Fermi_Hubbard_Model
An experimental open-source attempt to make GPT-4 fully autonomous.
Set of codes used to visualize spatial conductivity in the paper "Spatial Projection of Electronic Conductivity: The Example of Conducting Bridge Memory Materials
This project extends the idea of the innovative architecture of Kolmogorov-Arnold Networks (KAN) to the Convolutional Layers, changing the classic linear transformation of the convolution to learnable non linear activations in each pixel.
decodense: Bond- and atom-wise decompositions of HF and KS-DFT calculations
Implementation of deep implicit attention in PyTorch
A collection of phase locked loop (PLL) related projects
The entmax mapping and its loss, a family of sparse softmax alternatives.
The ESPResSo package
Extended Kalman Filter in Verilog
Links to all ten AI Club projects in Fall 2022. All projects will automatically enter a semester-long hackathon and compete for prizes.
It is the Final project
This is an implementation in Verilog of Super Mario Bros console game levels to be uploaded in a FPGA card.
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.
Sample Code for Gated Graph Neural Networks
Use GNN for predicting molecular properties and energy
converting HEIC to PDF file
HW PHY480/905
Code used to clean the data
Plot fitting method for josephson junction
Keithley Instruments reference and support code
Python drivers for lab hardware
Animation engine for explanatory math videos
Many Body using neural networks
Continuum mechanics toolkit
Open source implementation of "Neural Message Passing for Quantum Chemistry"
Repository for "Machine learning microscopic form of nematic order in twisted double-bilayer graphene"