4th PhyNuBe school : Innovative Technologies, Theories, and Methodologies in Nuclear Physics

Tutorials

Page under construction

This page will includes practical information for volunteers who decided to attend the tutorial sessions. 

Machine learning and artificial intelligence in experiment and theory

• Requirements : 

Bayesian methods to predict physics phenomena from observations and theories

• Requirements : 

Quantum computing

This tutorial is designed for participants who are \emph{not} quantum computing experts. Examples will be made using the package qiskit (provided by the IBM company). 
The participants will have their own laptops with some packages already installed 
on their labtop prior to coming to the tutorial.

Installing a dedicated environment using conda  (optional)

Ideally, the installation can be made using conda and jupyter notebook that allows to create a dedicated environment. If you are not using conda, you can create the environment here called "Tuto-QC" using the following commands 

 conda create -n Tuto-QC python=3.11  
 source activate Tuto-QC
 pip install jupyter 
 pip install notebook
 pip install jupyterlab
 pip install matplotlib

Once install, jupyter notebook or jupyter lab can be launched simply by typing jupyter notebook or  jupyter lab. 

 

Installing qiskit:

To install qiskit, in the environment install qiskit  

If the environment "Tuto-QC" is activated (see above), type:

 pip install qiskit
 pip install qiskit-aer matplotlib numpy
 pip install pylatexenc

 
Then test that the command

from qiskit import QuantumCircuit

runs successfully in python command line or in a jupyter notebook. 

 

The requirements for package versions are:

Requirements :

Recommended Python version: 3.10+

qiskit==2.4.0
qiskit-aer>=0.17
jupyterlab>=4.0
notebook>=7.0
ipykernel>=6.0
matplotlib>=3.8
numpy>=1.26
scipy>=1.13