[https://excalidraw.com/#json=XBnibVJopP0Gr8kOZ-69F,DMmLNTof7_3ff3TVo4K_0Q]
Towards efficient and generic entanglement detection by machine learning.
Jue Xu and Qi Zhao, 2022
- anaconda
- python, version > 3.8.8
- numpy, version > 1.21.5
- matplotlib, version > 3.5.1
- QuTiP, version > 4.7.0
- Scikit-learn, version > 1.1.2
Installation instructions available at (https://qutip.org/docs/latest/installation). Reference:
- basic
- measurement, https://qutip.org/docs/latest/guide/guide-measurement.html
- random states
- gates and circuits: https://qutip.org/docs/latest/apidoc/functions.html?#quantum-information-processing, https://qutip.org/docs/latest/guide/qip/qip-simulator.html?#operator-level-circuit-simulation, https://qutip-qip.readthedocs.io/en/stable/qip-basics.html
- visualization of states
Installation instructions available at (https://scikit-learn.org/stable/install). Reference:
- SVM and Kernel for SVM
- feature elimination: sklearn.feature_selection.RFE, feature_selection
Classical shadow: hsinyuan-huang/predicting-quantum-properties; charleshadfield/adaptiveshadows
Unfaithful states: BenDive/Entanglement-Detection-Beyond-Measuring-Fidelities
Neural Network for entanglement classification: Matlab Code
The Code folder includes the code supporting the paper.
The script and notebook are organized as follows.
quantum_state_utils.py contains the functions that generate specific data (e.g. bi-separable, genuine entangled quantum states) and construct the training/testing datasets.
The .py script must be in the same directory as the notebook when they are run.
The entangle_detection.ipynb notebook contains the scripts that generates a particular example or a figure.
The classical_shadow.ipynb notebook demonstrates the performance of classical shadow methods.
The code here is made available under the Creative Commons Attribution license (CC BY 4.0)