13 citations · 33 across the 6 of their papers we have counts for
6 papers
Quantum Error Correction and Detection for Quantum Machine Learning
Eromanga Adermann, Haiyue Kang, Martin Sevior +1
At the intersection of quantum computing and machine learning, quantum machine learning (QML) is poised to revolutionize artificial intelligence. However, the vulnerability of the…
Almost fault-tolerant quantum machine learning with drastic overhead reduction
Haiyue Kang, Younghun Kim, Eromanga Adermann +2
Errors in the current generation of quantum processors pose a significant challenge towards practical-scale implementations of quantum machine learning (QML) as they lead to traina…
Variational Quantum Machine Learning with Quantum Error Detection
Eromanga Adermann, Hajime Suzuki, Muhammad Usman
Quantum machine learning (QML) is an emerging field that promises advantages such as faster training, improved reliability and superior feature extraction over classical counterpar…
Radio Signal Classification by Adversarially Robust Quantum Machine Learning
Yanqiu Wu, Eromanga Adermann, Chandra Thapa +3
Radio signal classification plays a pivotal role in identifying the modulation scheme used in received radio signals, which is essential for demodulation and proper interpretation…
Cosmic Voids in Evolving Dark Sector Cosmologies: the High Redshift Universe
Eromanga Adermann, Pascal J Elahi, Geraint F Lewis +1
We compare the evolution of voids formed under the standard cosmological model and two alternative cosmological models. The two models are a quintessence model (CDM) and a Coupl…
Cosmic Voids in Evolving Dark Sector Cosmologies: the Low Redshift Universe
Eromanga Adermann, Pascal J. Elahi, Geraint F. Lewis +1
We present a comparison of void properties between the standard model of cosmology, Cold Dark Matter (CDM), and two alternative cosmological models with evolving and interac…