Publication
Learning Fourier series with parametrized quantum circuits
Dirk Heimann; Hans Hohenfeld; Gunnar Schönhoff; Elie Mounzer; Frank Kirchner
In: Physical Review Research, Vol. 7, Page n.n. American Physical Society, 5/2025.
Abstract
Variational quantum algorithms (VQAs) and their applications in the field of quantum machine
learning through parametrized quantum circuits (PQCs) are thought to be one major way of leveraging noisy intermediate-scale quantum computing devices. However, differences in the performance
of certain VQA architectures are often unclear since established best practices, as well as detailed
studies, are missing. In this paper, we build upon the work by Schuld et al. [Phys. Rev. A 103,
032430 (2021)] and Vidal et al. [Front. Phys. 8, 297 (2020)] by comparing how well popular ans¨atze
for PQCs learn different one-dimensional truncated Fourier series. We also examine dissipative
quantum neural networks (dQNN) as introduced by Beer et al. [Nat. Commun. 11, 808 (2020)] and
propose a data reupload structure for dQNNs to increase their capability for this regression task.
By comparing the results for different PQC architectures, we can provide guidelines for designing
efficient PQCs.
Projects
- Q3-UP - Bedarfsorientierte und niederschwellige Qualifikationsbausteine für Quantencomputing und quantenmaschinelles Lernen
- QuDa-KI - Qubit-based data representations for classical machine learning and simulations