Schabolon / Approximate-Quantumness-Certification-of-NISQ-Devices-using-Machine-Learning

Approximate Quantumness Certification of NISQ Devices using Machine Learning

Overview

• code/: all the python code and data for training and evaluating the ML model.
  • visualization/: images of quantum circuits, quantum gates and histograms.
  • results/: files containing accuracy measurements for the different models.
  • data/: training data.
  • src/: python code.
    • model/: contains code for training and evaluating the different machine learning models
    • quantum_circuits/: Qiskit implementations for the quantum circuits.
    • visualization/: code for generating visualizations for quantum circuits, gates and histograms.
    • circuit_run_data.py: manages the data for a single circuit + backend.
    • dataset.py: manages a dataset. Generates simulator data if needed.
    • main.py: start evaluating the machine learning models from here.
    • quantum_backend_type.py: enum to manage different labels and folder names for quantum computers and simulators.
    • quantum_backends.py: enum of quantum backends (simulators and quantum computers).
    • simulator.py: generate training data by simulating quantum circuits.
• paper/: the source file for building the paper as pdf.

Setup

Prerequisites

• Python >= 3.11
• typst >= 0.10.0 (only for generating the paper)

Installation

Good Practice: Create and enter a virtual python environment.

• Creation: python -m venv venv
• Activation:
  • Windows: cmd: venv\Scripts\activate.bat or PowerShell: venv\Scripts\Activate.ps1
  • Linux/MacOS: run source venv/bin/activate

Install required packages: pip install -r requirements.txt

Usage

• (Activate virtual environment if necessary)
• Execute .py files by entering the corresponding folder and running python <filename>.py