This is the official repository of the dataset for the paper https://openreview.net/forum?id=vrBVFXwAmi.
The generated dataset can be download in the link.
To unzip the .zip
file, run the code:
unzip dataset.zip
The folder includes:
dataset
├── Rydberg (dataset of Rydberg Atom model)
│ ├── pretrain (dataset used for pretraining)
│ │ ├── qxx (dataset of the system with xx qubits)
│ │ │ ├── conditions.csv (values of physical conditions for each sample)
│ │ │ └── x.csv (measurement strings for the x-th sample)
│ ├── finetune (dataset used for finetuning)
│ │ ├── train (dataaset used for training)
│ │ │ ├── qxx (dataset of the system with xx qubits)
│ │ │ │ ├── mxx (dataset with xx random measurements)
│ │ │ │ │ ├── nxx (dataset with xx samples)
│ │ │ │ │ │ ├── conditions.csv
│ │ │ │ │ │ ├── x.csv
│ │ │ │ │ │ └── labels (gound truth labels)
│ │ ├── test (dataaset used for evaluation)
│ │ │ ├── qxx (dataset of the system with xx qubits)
│ │ │ │ ├── mxx (dataset with xx random measurements)
│ │ │ │ │ ├── nxx (dataset with xx samples)
│ │ │ │ │ │ ├── conditions.csv
│ │ │ │ │ │ ├── x.csv
│ │ │ │ │ │ └── labels (gound truth labels)
├── Heisenberg (dataset of anisotropic Heisenberg model)
│ ├── pretrain (dataset used for pretraining)
│ │ ├── qxx (dataset of the system with xx qubits)
│ │ │ ├── conditions.csv (values of physical conditions for each sample)
│ │ │ ├── x.csv (measurement strings for the x-th sample)
│ ├── finetune (dataset used for finetuning)
│ │ ├── correlation (dataset for correlation function prediction)
│ │ │ ├── train (dataaset used for training)
│ │ │ │ ├── qxx (dataset of the system with xx qubits)
│ │ │ │ │ ├── mxx (dataset with xx random measurements)
│ │ │ │ │ │ ├── nxx (dataset with xx samples)
│ │ │ │ │ │ │ ├── conditions.csv
│ │ │ │ │ │ │ ├── x.csv
│ │ │ │ │ │ │ └── labels (gound truth labels)
│ │ │ ├── test (dataaset used for evaluation)
│ │ │ │ ├── qxx (dataset of the system with xx qubits)
│ │ │ │ │ ├── mxx (dataset with xx random measurements)
│ │ │ │ │ │ ├── nxx (dataset with xx samples)
│ │ │ │ │ │ │ ├── conditions.csv
│ │ │ │ │ │ │ ├── x.csv
│ │ │ │ │ │ │ └── labels (gound truth labels)
│ │ ├── entropy (dataset for entanglement entropy prediction)
│ │ │ ├── train (dataaset used for training)
│ │ │ │ ├── qxx (dataset of the system with xx qubits)
│ │ │ │ │ ├── mxx (dataset with xx random measurements)
│ │ │ │ │ │ ├── nxx (dataset with xx samples)
│ │ │ │ │ │ │ ├── conditions.csv
│ │ │ │ │ │ │ ├── x.csv
│ │ │ │ │ │ │ └── labels (gound truth labels)
│ │ │ ├── test (dataaset used for evaluation)
│ │ │ │ ├── qxx (dataset of the system with xx qubits)
│ │ │ │ │ ├── mxx (dataset with xx random measurements)
│ │ │ │ │ │ ├── nxx (dataset with xx samples)
│ │ │ │ │ │ │ ├── conditions.csv
│ │ │ │ │ │ │ ├── x.csv
└── └── └── └── └── └── └── └── labels (gound truth labels)
A demo used to generate small-size dataset of the anisotropic Heisenberg model is provided in generate_heisenberg.py
. Users can adjust parameters such as the Hamiltonian, the number of qubits, the number of measurements, and the size of samples to obtain a customized dataset.