QPSK Signal Classification using Variational Quantum Classifier (VQC)

This project, created by Jesús Vilela Jato, is dedicated to simulating a Quadrature Phase Shift Keying (QPSK) signal with added noise, and classifying the signal-to-noise ratio (SNR) of each packet into two classes based on a certain threshold. A Variational Quantum Classifier (VQC) is trained on this data, and its performance is evaluated on a separate test set.

Prerequisites

The project is implemented in Python and requires the following libraries:

• Qiskit
• Numpy
• Scikit-learn

Installation

You can install the required packages using pip:

pip install qiskit numpy scikit-learn

Running the Code

To run the code, simply navigate to the directory containing the script and run:

python main.py

Project Structure

The project consists of the following steps:

1. Simulate a QPSK signal and add noise: Generates a QPSK signal with noise.
2. Calculate SNR and define labels: Calculates the SNR for each packet and labels it based on the SNR threshold.
3. Prepare data for VQC: The labels are converted to strings, and a feature map, a variational form, and an optimizer are created.
4. Train the VQC: The VQC is trained on the training set and the parameters of the variational form are adjusted to minimize the loss function.
5. Test the VQC: The VQC is tested on the test set, and the accuracy of the model is calculated.

License

Copyright (c) 2023 Jesús Vilela Jato. All Rights Reserved.

Distributed under the MIT license. See LICENSE for more information.