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Quantum Kernel Discovery for Anomaly Detection

Introduction

This repository contains the implementation of a Quantum Kernel Discovery pipeline for Anomaly Detection.

Environment Setup

Before running the pipeline, ensure that the environment is correctly set up by installing the required packages.

Using pip

Install the dependencies from the requirements.txt file using pip:

pip install -r requirements.txt

Using conda

Alternatively, create a conda environment using the environment.yml file:

conda env create -f environment.yml

Data Acquisition

The dataset can be downloaded from the following link: Dataset. After downloading, move the data into the QML_paper_data folder for further processing.

Quantum Kernel Discovery Pipeline

Execute the pipeline using the command below. The CUSTOM mode activates the kernel discovery algorithm.

python main.py mode [--latent_dim VALUE] [--opt_param VALUE] [--n_op VALUE] [--ntest VALUE] [--ntrain VALUE] [--final_train VALUE] [--final_test VALUE]

Arguments:

  • --latent_dim VALUE: Set the latent dimension.
  • --opt_param VALUE: Define the number of Bayesian optimization epochs.
  • --n_op VALUE: Determine the number of operations in the kernel.
  • --ntest VALUE: Specify the number of samples for model evaluation.
  • --ntrain VALUE: Set the number of samples for training the model.
  • --final_train VALUE: Indicate the training sample size for the final model.
  • --final_test VALUE: Set the evaluation sample size for the final model.

Model Comparison

To compare the performance of different models:

python main.py mode [--latent_dim VALUE] [--final_train VALUE] [--final_test VALUE]

Modes:

  • FIXED: Use the predefined quantum model proposed in [paper di woz et al.].
  • CLASSICAL: Use a classical OC-SVM.

Visualization

For generating plots and graphs, follow these instructions:

Preparing Plots

python prepare_plot_scores.py --quantum_folder [PATH] --comparison_folder [PATH] --mode [MODE] --out_path [PATH] --name_suffix [SUFFIX]

Parameters:

  • --quantum_folder PATH: Path to the quantum model's folder.
  • --comparison_folder PATH: Path to the comparison model's folder.
  • --mode MODE: Model type (CLASSICAL or FIXED).
  • --out_path PATH: Base path for the output files.
  • --name_suffix SUFFIX: Suffix for the output filenames.

Creating Final Plots

python create_plot.py --file_name [NAME] --mode [MODE] --latent_dim [VALUE]

Arguments:

  • --file_name NAME: Name of the file from the previous step.
  • --mode MODE: Type of model (CLASSICAL or FIXED).
  • --latent_dim VALUE: The latent dimension used.

Reproducible Results

To reproduce our findings, use the following commands:

python main.py CUSTOM --latent_dim 8 --final_train 200 --final_test 1500 --n_op 12 --opt_param 10 --ntest 75 --ntrain 75
python main.py FIXED --latent_dim 8 --final_train 200 --final_test 1500

python prepare_plot_scores.py --quantum_folder trained_qsvms/model_testing_L8_T200 --comparison_folder trained_qsvms/fixed_testing_L8_T200 --mode FIXED --out_path plot --name_suffix n1500_k5

python create_plot.py --file_name plot_comparison_fixed --mode FIXED --latent_dim 8