QML Essentials -- A framework for working with Quantum Fourier Models

TL;DR

This paper introduces a Python framework built on PennyLane for analyzing Quantum Machine Learning models, especially Quantum Fourier Models, enabling easier evaluation, noise simulation, and spectrum analysis.

Contribution

It provides a comprehensive toolkit for Quantum Fourier Model analysis, including spectrum calculation, noise simulation, and predefined models, enhancing research efficiency.

Findings

Framework supports noise addition and parameter initialization strategies.

Includes methods for Fourier spectrum calculation via FFT and analytical expansion.

Facilitates rapid implementation and analysis of Quantum Machine Learning models.

Abstract

In this work, we propose a framework in the form of a Python package, specifically designed for the analysis of Quantum Machine Learning models. This framework is based on the PennyLane simulator and facilitates the evaluation and training of Variational Quantum Circuits. It provides additional functionality ranging from the ability to add different types of noise to the classical simulation, over different parameter initialisation strategies, to the calculation of expressibility and entanglement for a given model. As an intrinsic property of Quantum Fourier Models, it provides two methods for calculating the corresponding Fourier spectrum: one via the Fast Fourier Transform and another analytical method based on the expansion of the expectation value using trigonometric polynomials. It also provides a set of predefined approaches that allow a fast and straightforward implementation of Quantum Machine Learning models. With this framework, we extend the PennyLane simulator with a set of tools that allow researchers a more convenient start with Quantum Fourier Models and aim to unify the analysis of Variational Quantum Circuits.