asemposki Goto Github PK

2023-frib-ta-summer-school

Repository for the 2023 FRIB-TA Summer School on practical uncertainty quantification and emulation!

asemposki

Personal repository about me and my work!

bandframework

This contains the public repository for the BAND framework project.

bandframework.github.io

Website fork for BAND.

bayes2019

This repository contains the learning material for the Nuclear TALENT course Learning from Data: Bayesian Methods and Machine Learning, in York, UK, June 10-28, 2019.

cs2400

deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

eigenvector-continuation

evidence-and-model-selection

A repo containing my work on model selection in Bayesian statistics and calculating evidences.

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.

git-lab

git-lab-2

ibm-quantum-challenge-fall-22

For IBM Quantum Fall Challenge 2022

nuclear-quantum-computing

Our winning project from the FRIB-TA Summer School on Quantum Computing!

qc_spr_22_hw

A repo for storing my homework assignments in Quantum Computing (Spring 2022).

samba

The toddler BAND package which performs Bayesian model mixing upon a toy model setup.

simple_bayes_with_gp

taweret

Python package for Bayesian Model Mixing