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
The tutorial session will be conducted by Denis Lacroix with the precious help of Ashutosh Singh (Postdoc IJCLab) and Rémi Bocquet (PhD CEA DAM).
This tutorial is designed for participants who are 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 matplotlibOnce 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 commandfrom 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