Resource-efficient Quantum Neuron for Quantum Neural Networks

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Abstract

Classical neural networks (CNNs) provide wide applications in communication technology, ranging from enhancement of key performance indicators (KPIs) of communication protocols to utilities in intelligent networks. Quantum neural networks (QNNs) have advantages over CNNs for applications in future communication networks. This article surveys existing methods for implementing QNNs and suggests a novel resource-friendly quantum system with enhanced activation for better performance in QNNs. Our approach is motivated by a principle of neuronal gain control established in neuroscience and was implemented on a quantum computer using IBM's qiskit. Potential implications of such a model for communication technology are discussed.

Details

OriginalspracheEnglisch
Titel2023 IEEE Globecom Workshops, GC Wkshps 2023
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten1045-1050
Seitenumfang6
ISBN (elektronisch)979-8-3503-7021-8
PublikationsstatusVeröffentlicht - 2023
Peer-Review-StatusJa

Konferenz

Titel2023 IEEE Globecom Workshops, GC Wkshps 2023
Dauer4 - 8 Dezember 2023
StadtKuala Lumpur
LandMalaysia

Externe IDs

ORCID /0000-0001-8409-5390/work/158767933

Schlagworte

Schlagwörter

  • CNNs, gain control, non-linear activation functions, QNNs, quantum neuron