ScarProject

Simulations of quantum many-body systems focusing on the PXP model, Rydberg chains, Schrieffer-Wolff (SW) corrections, and constrained subspace analysis. This project includes dynamics, entanglement entropy calculations, overlap with eigenstates, and studies of projected and non-projected perturbations.

📂 Project Structure

plaintext ScarProject/ ├── models/ # Hamiltonians, operators, and initial states ├── utils/ # Auxiliary functions (save, plot, entropy) ├── run/ # Executable scripts ├── tests/ # Test scripts and usage examples ├── results/ # Automatically generated data │ ├── ResultsEvolution/ │ │ ├── PXP/ │ │ ├── PXPSW/ │ │ ├── Rydberg/ │ │ └── Perturbation/ │ ├── ResultsEntropy/ │ └── ResultsSubspace/ ├── .vscode/ # VS Code settings (optional) ├── README.md # This file ├── requirements.txt # Dependencies ├── setup.py # Optional installation as a package

🚀 How to Run

Run from the project root:

bash python run/run_pxp.py python run/run_pxp_perturbation.py python run/run_pxp_sw.py python run/run_rydberg.py python run/run_entropy.py python run/run_overlap_plot.py

✅ IMPORTANT: Always run from the project root ScarProject/.
✔️ If using VS Code, it works perfectly with the PLAY button if you have .vscode/settings.json configured or the magic line sys.path.append() at the top of the scripts.

🧠 Features

• ✅ PXP model time evolution
• ✅ PXP with projected and non-projected perturbations (X, Y, Z, or all directions)
• ✅ PXP with second-order Schrieffer-Wolff (SW) corrections
• ✅ Rydberg chain with blockade constraint
• ✅ Entanglement entropy calculation over time
• ✅ Overlap analysis between initial states and eigenstates in the constrained subspace
• ✅ Automatic data organization
• ✅ Publication-ready plotting tools

🧠 Requirements

Install dependencies with:

bash pip install -r requirements.txt

Dependencies:

• numpy
• qutip
• matplotlib

🗃️ Data Organization

Data is automatically saved under /results/:

• 📁 ResultsEvolution/
  • 🔸 PXP/
  • 🔸 PXPSW/
  • 🔸 Rydberg/
  • 🔸 Perturbation/
• 📁 ResultsEntropy/
• 📁 ResultsSubspace/

🔧 Install as a Package (optional)

Install locally as a package if desired:

bash pip install .

👨‍💻 Author

• 🔬 Code development, organization, and simulations by Alessandra Chioquetta.

📜 License

MIT License — free to use, modify, and contribute.

✔️ Note: This project is professionally structured to be replicable, scalable, and ready for academic use (thesis, paper, presentation) and public repositories such as GitHub and PyPI.