supervised

Tensor Network for Anomaly Detection in Latent Space of Proton-Proton Collision Events at the LHC

Implementation of breast classification task from the paper: 2502.13090.

Install tn4ml directly from GitHub:

git clone https://github.com/bsc-quantic/tn4ml.git

Install an additional package for data handling:

• h5py

Download dataset

The dataset can be download from kaggle.com/breast-cancer/data.

Run training and evaluation pipeline

Data Parameters

• save_dir (str): Path to directory for saving results (default = "results")
• load_dir (str): Path to directory for loading the data

MPS Parameters

• bond_dims (int): Bond dimensions of per each MPS (default = [2, 4, 8, 16, 32])

Training Parameters

• lr (float): Learning rate (default = 1e-3)
• min_delta (float): Minimum improvement required for early stopping (default = 0)
• patience (int): Number of epochs with no improvement before early stopping (default = 20)
• epochs (int): Maximum number of training epochs (default = 100)
• batch_size (int): Number of samples per training batch (default = 32)
• test_batch_size (int): Number of samples per training batch (default = 64)

python breast_class.py -save_dir results \
                   -device cpu\
                   -load_dir data \
                   -bond_dims 2 5 10 50\
                   -lr 0.001 \
                   -patience 25 \
                   -epochs 100 \
                   -batch_size 32 \
                   -test_batch_size 32 \