Resource-efficient Quantum Neuron for Quantum Neural Networks
Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/Gutachten › Beitrag in Konferenzband › Beigetragen › Begutachtung
Beitragende
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
Originalsprache | Englisch |
---|---|
Titel | 2023 IEEE Globecom Workshops, GC Wkshps 2023 |
Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
Seiten | 1045-1050 |
Seitenumfang | 6 |
ISBN (elektronisch) | 979-8-3503-7021-8 |
Publikationsstatus | Veröffentlicht - 2023 |
Peer-Review-Status | Ja |
Konferenz
Titel | 2023 IEEE Globecom Workshops, GC Wkshps 2023 |
---|---|
Dauer | 4 - 8 Dezember 2023 |
Stadt | Kuala Lumpur |
Land | Malaysia |
Externe IDs
ORCID | /0000-0001-8409-5390/work/158767933 |
---|
Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- CNNs, gain control, non-linear activation functions, QNNs, quantum neuron